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\n  \n 2024\n \n \n (3)\n \n \n
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\n \n\n \n \n Ahn, Y., & Lin, Y.\n\n\n \n \n \n \n \n Break Out of a Pigeonhole: A Unified Framework for Examining Miscaliberation, Bias and Stereotype in Recommender Systems.\n \n \n \n \n\n\n \n\n\n\n ACM Transactions on Intelligent Systems and Technology (TIST). 2024.\n \n\n\n\n
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@article{tengVISPURVisualAids2023,\n  title = {{{Break Out}} of a {{Pigeonhole}}: {{A Unified Framework}} for {{Examining Miscaliberation, Bias}} and {{Stereotype}} in {{Recommender Systems}}},\n  author = {Ahn, Yongsu and Lin, Yu-Ru},\n  year = {2024},\n  journal = {ACM Transactions on Intelligent Systems and Technology (TIST)},\n  url = {https://arxiv.org/pdf/2312.17443.pdf}\n}\n\n
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\n \n\n \n \n Diab, A., Rr, N., & Lin, Y.\n\n\n \n \n \n \n \n Classifying Conspiratorial Narratives At Scale: False Alarms and Erroneous Connections.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the 18th International AAAI Conference on Weblogs and Social Media (ICWSM 2024), 2024. \n \n\n\n\n
\n\n\n\n \n \n \"ClassifyingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{diabConspiratorialNarratives2024,\n  title = {Classifying {{Conspiratorial Narratives At Scale: False Alarms}} and {{Erroneous Connections}}},\n  booktitle = {Proceedings of the 18th {{International AAAI Conference}} on {{Weblogs}} and {{Social Media}} ({{ICWSM}} 2024)},\n  author = {Diab, Ahmad and Rr, Nefriana and Lin, Yu-Ru},\n  year = {2024},\n  url = {https://arxiv.org/pdf/2404.00141.pdf}\n}\n \n
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\n \n\n \n \n Yan, M., Chiang, A. Y., & Lin, Y.\n\n\n \n \n \n \n From Posts to Pavement, or Vice Versa? The Dynamic Interplay Between Online Activism and Offline Confrontations.\n \n \n \n\n\n \n\n\n\n In Proceedings of the 18th International AAAI Conference on Web and Social Media (ICWSM 2024), 2024. \n \n\n\n\n
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@inproceedings{yanOnlineOfflineConfrontations2024,\n  title = {From {{Posts}} to {{Pavement}}, or {{Vice Versa? The Dynamic Interplay Between Online Activism}} and {{Offline Confrontations}}},\n  booktitle = {Proceedings of the 18th {{International AAAI Conference}} on {{Web}} and {{Social Media}} ({{ICWSM}} 2024)},\n  author = {Yan, Muheng and Chiang, Amy Yunyu and Lin, Yu-Ru},\n  year = {2024},\n}\n\n
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\n \n\n \n \n Teng, X., Ahn, Y., & Lin, Y.\n\n\n \n \n \n \n \n VISPUR: Visual Aids for Identifying and Interpreting Spurious Associations in Data-Driven Decisions.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Visualization and Computer Graphics (TVCG),1–1. 2023.\n \n\n\n\n
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@article{tengVISPURVisualAids2023,\n  title = {{{VISPUR}}: {{Visual Aids}} for {{Identifying}} and {{Interpreting Spurious Associations}} in {{Data-Driven Decisions}}},\n  shorttitle = {{{VISPUR}}},\n  author = {Teng, Xian and Ahn, Yongsu and Lin, Yu-Ru},\n  year = {2023},\n  journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)},\n  pages = {1--1},\n  url = {https://arxiv.org/pdf/2307.14448.pdf}\n}\n\n
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\n \n\n \n \n Lin, Y., Wu, S., & Mason, W.\n\n\n \n \n \n \n \n Mapping Language Literacy at Scale: A Case Study on Facebook.\n \n \n \n \n\n\n \n\n\n\n EPJ Data Science. 2023.\n \n\n\n\n
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@article{linMappingLanguageLiteracy2023,\n  title = {Mapping Language Literacy at Scale: A Case Study on {{Facebook}}},\n  author = {Lin, Yu-Ru and Wu, Shaomei and Mason, Winter},\n  year = {2023},\n  journal = {EPJ Data Science},\n  doi = {10.1140/epjds/s13688-023-00388-4},\n  url = {https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-023-00388-4}\n}\n\n
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\n \n\n \n \n Yoder, M. M., Diab, A., Brown, D. W., & Carley, K. M.\n\n\n \n \n \n \n \n A Weakly Supervised Classifier and Dataset of White Supremacist Language.\n \n \n \n \n\n\n \n\n\n\n ACL, abs/2306.15732. 2023.\n \n\n\n\n
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@article{Yoder2023AWS,\n  title={A Weakly Supervised Classifier and Dataset of White Supremacist Language},\n  author={Michael Miller Yoder and Ahmad Diab and David West Brown and Kathleen M. Carley},\n  journal={ACL},\n  year={2023},\n  volume={abs/2306.15732},\n  doi = {10.48550/arXiv.2306.15732},\n  url = {https://arxiv.org/pdf/2306.15732.pdf}\n}\n\n
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\n \n\n \n \n Ahn, Y., Yan, M., Lin, Y., & Wang, Z.\n\n\n \n \n \n \n \n HungerGist: An Interpretable Predictive Model for Food Insecurity.\n \n \n \n \n\n\n \n\n\n\n In IEEE International Conference on Big Data (Big Data), 2023. IEEE\n \n\n\n\n
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@inproceedings{ahnHungerGist2023,\n  title = {{{HungerGist}}: {{An Interpretable Predictive Model}} for {{Food Insecurity}}},\n  shorttitle = {{{HungerGist}}},\n  booktitle = {IEEE International Conference on Big Data (Big Data)},\n  author = {Ahn, Yongsu and Yan, Muheng and Lin, Yu-Ru and Wang, Zian},\n  year = {2023},\n  publisher = {{IEEE}},\n  url = {https://arxiv.org/pdf/2311.10953.pdf}\n}\n\n
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\n \n\n \n \n Biswas, A., Niven, T., & Lin, Y.\n\n\n \n \n \n \n \n The Dynamics of Political Narratives During the Russian Invasion of Ukraine.\n \n \n \n \n\n\n \n\n\n\n In International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2023), 2023. \n \n\n\n\n
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@inproceedings{biswasDynamics2023,\n  title = {The {{Dynamics}} of {{Political Narratives During}} the {{Russian Invasion}} of {{Ukraine}}},\n  booktitle = {{{International Conference}} on {{Social Computing}}, {{Behavioral-Cultural Modeling}} \\& {{Prediction}} and {{Behavior Representation}} in {{Modeling}} and {{Simulation}} ({{SBP-BRiMS}} 2023)},\n  author = {Biswas, Ahana and Niven, Tim and Lin, Yu-Ru},\n  year = {2023},\n  url = {https://arxiv.org/pdf/2307.13753.pdf}\n}\n\n
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\n \n\n \n \n Ahn, Y., Lin, Y., Xu, P., & Dai, Z.\n\n\n \n \n \n \n \n ESCAPE: Countering Systematic Errors from Machine's Blind Spots via Interactive Visual Analysis.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2023), 2023. ACM\n \n\n\n\n
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@inproceedings{ahnESCAPECounteringSystematic2023,\n  title = {{{ESCAPE}}: {{Countering Systematic Errors}} from {{Machine}}'s {{Blind Spots}} via {{Interactive Visual Analysis}}},\n  shorttitle = {{{ESCAPE}}},\n  booktitle = {Proceedings of the {{ACM SIGCHI Conference}} on {{Human Factors}} in {{Computing Systems}} ({{CHI}} 2023)},\n  author = {Ahn, Yongsu and Lin, Yu-Ru and Xu, Panpan and Dai, Zeng},\n  year = {2023},\n  publisher = {{ACM}},\n  url = {https://arxiv.org/pdf/2303.09657.pdf},\n  url_Video = {https://www.youtube.com/watch?v=A5yk6vW402U}\n}\n\n
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\n \n\n \n \n Diab, A., Jagdagdorj, B., Ng, L. H. X., Lin, Y., & Yoder, M. M.\n\n\n \n \n \n \n \n Online to Offline Crossover of White Supremacist Propaganda.\n \n \n \n \n\n\n \n\n\n\n In Companion Proceedings of the Web Conference 2023 (WWW '23 Companion), 2023. ACM\n \n\n\n\n
\n\n\n\n \n \n \"OnlinePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 10 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{diabOnlineOfflineCrossover2023,\n  title = {Online to {{Offline Crossover}} of {{White Supremacist Propaganda}}},\n  booktitle = {Companion {{Proceedings}} of the {{Web Conference}} 2023 ({{WWW}} '23 {{Companion}})},\n  author = {Diab, Ahmed and Jagdagdorj, Bolor-Erdene and Ng, Lynnette Hui Xian and Lin, Yu-Ru and Yoder, Michael Miller},\n  year = {2023},\n  publisher = {{ACM}},\n  doi = {10.48550/arXiv.2303.07838},\n  url = {https://arxiv.org/pdf/2303.07838.pdf}\n}\n\n
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\n \n\n \n \n Ahn, Y., Yan, M., Lin, Y., Chung, W., & Hwa, R.\n\n\n \n \n \n \n \n Tribe or Not? Critical Inspection of Group Differences Using TribalGram.\n \n \n \n \n\n\n \n\n\n\n ACM Transactions on Interactive Intelligent Systems (TiiS), 12(1): 1–34. 2022.\n \n\n\n\n
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@article{ahnTribeNotCritical2022a,\n  title = {Tribe or {{Not}}? {{Critical Inspection}} of {{Group Differences Using TribalGram}}},\n  shorttitle = {Tribe or {{Not}}?},\n  author = {Ahn, Yongsu and Yan, Muheng and Lin, Yu-Ru and Chung, Wen-Ting and Hwa, Rebecca},\n  year = {2022},\n  journal = {ACM Transactions on Interactive Intelligent Systems (TiiS)},\n  volume = {12},\n  number = {1},\n  pages = {1--34},\n  publisher = {{ACM New York, NY}},\n  url = {https://arxiv.org/pdf/2303.09664.pdf}\n}\n\n
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\n \n\n \n \n Ahn, Y., Beigel, E., Braun, N., Griffin, C., Linardi, S., Mickles, B., & Rial, E.\n\n\n \n \n \n \n Improving Citizen-initiated Police Reform Efforts through Interactive Design: A Case Study in Allegheny County.\n \n \n \n\n\n \n\n\n\n In Equity and Access in Algorithms, Mechanisms, and Optimization, pages 1–10. 2022.\n \n\n\n\n
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@incollection{ahn2022improving,\n  title={Improving Citizen-initiated Police Reform Efforts through Interactive Design: A Case Study in Allegheny County},\n  author={Ahn, Yongsu and Beigel, Eliana and Braun, Noah and Griffin, Collin and Linardi, Sera and Mickles, Blair and Rial, Emmaline},\n  booktitle={Equity and Access in Algorithms, Mechanisms, and Optimization},\n  pages={1--10},\n  year={2022}\n}\n\n
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\n \n\n \n \n Li, A., Farzan, R., Lin, Y., Zhou, Y., Teng, X., & Yan, M.\n\n\n \n \n \n \n \n Identifying and Understanding Social Media Gatekeepers: A Case Study of Gatekeepers for Immigration Related News on Twitter.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the ACM on Human-Computer Interaction (CSCW 2022), 2022. ACM\n \n\n\n\n
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@inproceedings{liIdentifyingUnderstandingSocial2022,\n  title = {Identifying and {{Understanding Social Media Gatekeepers}}: {{A Case Study}} of {{Gatekeepers}} for {{Immigration Related News}} on {{Twitter}}},\n  booktitle = {Proceedings of the {{ACM}} on {{Human-Computer Interaction}} ({{CSCW}} 2022)},\n  author = {Li, Ang and Farzan, Rosta and Lin, Yu-Ru and Zhou, Yingfan and Teng, Xian and Yan, Muheng},\n  year = {2022},\n  publisher = {{ACM}},\n  url = {https://dl.acm.org/doi/pdf/10.1145/3555195}\n}\n\n\n
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\n \n\n \n \n Teng, X., Lin, Y., Chung, W., Li, A., & Kovashka, A.\n\n\n \n \n \n \n \n Characterizing User Susceptibility to COVID-19 Misinformation on Twitter.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the 16th International AAAI Conference on Web and Social Media (ICWSM 2022), 2022. AAAI\n \n\n\n\n
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@inproceedings{tengCharacterizingUserSusceptibility2022,\n  title = {Characterizing {{User Susceptibility}} to {{COVID-19 Misinformation}} on {{Twitter}}},\n  booktitle = {Proceedings of the 16th {{International AAAI Conference}} on {{Web}} and {{Social Media}} ({{ICWSM}} 2022)},\n  author = {Teng, Xian and Lin, Yu-Ru and Chung, Wen-Ting and Li, Ang and Kovashka, Adriana},\n  year = {2022},\n  publisher = {{AAAI}},\n  url = {https://arxiv.org/pdf/2109.09532.pdf},\n}\n\n
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\n \n\n \n \n Ertugrul, A. M., Lee, J., Wu, S., Lin, Y., & Xie, L.\n\n\n \n \n \n \n \n Whose Advantage? Measuring Attention Dynamics across YouTube and Twitter on Controversial Topics.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the 16th International AAAI Conference on Web and Social Media (ICWSM 2022), 2022. AAAI\n \n\n\n\n
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@inproceedings{ertugrulWhoseAdvantageMeasuring2022,\n  title = {Whose {{Advantage}}? {{Measuring Attention Dynamics}} across {{YouTube}} and {{Twitter}} on {{Controversial Topics}}},\n  booktitle = {Proceedings of the 16th {{International AAAI Conference}} on {{Web}} and {{Social Media}} ({{ICWSM}} 2022)},\n  author = {Ertugrul, Ali Mert and Lee, Jooyoung and Wu, Siqi and Lin, Yu-Ru and Xie, Lexing},\n  year = {2022},\n  publisher = {{AAAI}},\n  url = {https://arxiv.org/pdf/2204.00988.pdf},\n}\n\n
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\n \n\n \n \n Mesut Erhan, U., Kovashka, A., Chung, W., & Lin, Y.\n\n\n \n \n \n \n \n Visual Persuasion in COVID-19 Social Media Content: A Multi-Modal Characterization.\n \n \n \n \n\n\n \n\n\n\n In Companion Proceedings of the Web Conference 2022 (WWW '22 Companion), 2022. ACM\n \n\n\n\n
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@inproceedings{mesuterhanVisualPersuasionCOVID192022,\n  title = {Visual {{Persuasion}} in {{COVID-19 Social Media Content}}: {{A Multi-Modal Characterization}}},\n  booktitle = {Companion {{Proceedings}} of the {{Web Conference}} 2022 ({{WWW}} '22 {{Companion}})},\n  author = {Mesut Erhan, Unal and Kovashka, Adriana and Chung, Wen-Ting and Lin, Yu-Ru},\n  year = {2022},\n  publisher = {{ACM}},\n  url = {https://arxiv.org/pdf/2112.13910.pdf},\n}\n\n
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\n \n\n \n \n Yan, M., Chung, W., & Lin, Y.\n\n\n \n \n \n \n \n Are Mutated Misinformation More Contagious? A Case Study of COVID-19 Misinformation on Twitter.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of Web Science 2022 (WebSci 2022), 2022. \n \n\n\n\n
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@inproceedings{yanAreMutatedMisinformation2022,\n  title = {Are {{Mutated Misinformation More Contagious}}? {{A Case Study}} of {{COVID-19 Misinformation}} on {{Twitter}}},\n  booktitle = {Proceedings of {{Web Science}} 2022 ({{WebSci}} 2022)},\n  author = {Yan, Muheng and Chung, Wen-Ting and Lin, Yu-Ru},\n  year = {2022},\n  doi = {10.1145/3501247.3531562},\n  url = {https://dl.acm.org/doi/pdf/10.1145/3501247.3531562}\n}\n\n\n
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\n \n\n \n \n Pei, S., Teng, X., Lewis, P., & Shaman, J.\n\n\n \n \n \n \n Optimizing Respiratory Virus Surveillance Networks using Uncertainty Propagation.\n \n \n \n\n\n \n\n\n\n Nature Communications, 12(1): 1–10. 2021.\n \n\n\n\n
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@article{pei2021optimizing,\n  title={Optimizing Respiratory Virus Surveillance Networks using Uncertainty Propagation},\n  author={Pei, Sen and Teng, Xian and Lewis, Paul and Shaman, Jeffrey},\n  journal={Nature Communications},\n  volume={12},\n  number={1},\n  pages={1--10},\n  year={2021},\n  doi={https://doi.org/10.1038/s41467-020-20399-3},\n  abstract = {Infectious disease prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for diseases with limited records. The  proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens – human metapneumovirus and seasonal coronavirus – from 35 US states and can be used to guide systemic allocation of surveillance efforts.}\n}\n\n
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\n Infectious disease prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for diseases with limited records. The proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens – human metapneumovirus and seasonal coronavirus – from 35 US states and can be used to guide systemic allocation of surveillance efforts.\n
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\n \n\n \n \n Teng, X., Pei, S., & Lin, Y.\n\n\n \n \n \n \n \n StoCast: Stochastic Disease Forecasting with Progression Uncertainty.\n \n \n \n \n\n\n \n\n\n\n IEEE Journal of Biomedical and Health Informatics, 25(3): 850–861. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"StoCast:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{tengStoCastStochasticDisease2021,\n  title = {{{StoCast}}: {{Stochastic}} Disease Forecasting with Progression Uncertainty},\n  author = {Teng, Xian and Pei, Sen and Lin, Yu-Ru},\n  year = {2021},\n  journal = {IEEE Journal of Biomedical and Health Informatics},\n  volume = {25},\n  number = {3},\n  pages = {850--861},\n  issn = {2168-2208},\n  doi = {10.1109/JBHI.2020.3006719},\n  url = {https://bit.ly/stocast-deepgen},\n  abstract = {Forecasting patients' disease progressions with rich longitudinal clinical data has attracted much attention in recent years due to its potential application in healthcare. Researchers have tackled this problem by leveraging traditional machine learning, statistical techniques and deep learning based models. However, existing methods suffer from either deterministic internal structures or over-simplified stochastic components, failing to deal with complex uncertain scenarios such as progression uncertainty (i.e., multiple possible trajectories) and data uncertainty (i.e., imprecise observations and misdiagnosis). In the face of such uncertainties, we move beyond those formulations and ask a challenging question: What is the distribution of a patient's possible health states at a future time For this purpose, we propose a novel deep generative model, named Stochastic Disease Forecasting Model (StoCast), along with an associated neural network architecture, called StoCastNet, that can be trained efficiently via stochastic optimization techniques. Our StoCast model contains internal stochastic components that can tolerate departures of observed data from patients' true health states, and more importantly, is able to produce a comprehensive estimate of future disease progression possibilities. Based on two public datasets related to Alzheimer's disease and Parkinson's disease, we demonstrate that our StoCast model achieves robust and superior performance than deterministic baseline approaches, and conveys richer information that can potentially assist doctors to make decisions with greater confidence in a complex uncertain scenario.},\n  keywords = {Deep Generative Models,Disease Forecasting,Neural Networks,Progression Uncertainty}\n}\n\n
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\n Forecasting patients' disease progressions with rich longitudinal clinical data has attracted much attention in recent years due to its potential application in healthcare. Researchers have tackled this problem by leveraging traditional machine learning, statistical techniques and deep learning based models. However, existing methods suffer from either deterministic internal structures or over-simplified stochastic components, failing to deal with complex uncertain scenarios such as progression uncertainty (i.e., multiple possible trajectories) and data uncertainty (i.e., imprecise observations and misdiagnosis). In the face of such uncertainties, we move beyond those formulations and ask a challenging question: What is the distribution of a patient's possible health states at a future time For this purpose, we propose a novel deep generative model, named Stochastic Disease Forecasting Model (StoCast), along with an associated neural network architecture, called StoCastNet, that can be trained efficiently via stochastic optimization techniques. Our StoCast model contains internal stochastic components that can tolerate departures of observed data from patients' true health states, and more importantly, is able to produce a comprehensive estimate of future disease progression possibilities. Based on two public datasets related to Alzheimer's disease and Parkinson's disease, we demonstrate that our StoCast model achieves robust and superior performance than deterministic baseline approaches, and conveys richer information that can potentially assist doctors to make decisions with greater confidence in a complex uncertain scenario.\n
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\n \n\n \n \n Yan, M., Lin, Y., & Litman, D.\n\n\n \n \n \n \n \n Argumentatively Phony? Detecting Misinformation via Argument Mining.\n \n \n \n \n\n\n \n\n\n\n In KDD Workshop on AI-enabled Cybersecurity Analytics (AI4Cyber 2021), 2021. ACM\n \n\n\n\n
\n\n\n\n \n \n \"ArgumentativelyPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{yanArgumentativelyPhonyDetecting2021,\n  title = {{Argumentatively Phony? Detecting Misinformation via Argument Mining}},\n  booktitle = {{KDD Workshop on AI-enabled Cybersecurity Analytics (AI4Cyber 2021)}},\n  author = {Yan, Muheng and Lin, Yu-Ru and Litman, Diane},\n  year = {2021},\n  publisher = {{ACM}},\n  url = {https://www.ai4cyber-kdd.com/KDD-AISec_files/Paper_ArgumentMisinfo-KDD.pdf},\n}\n\n
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\n  \n 2020\n \n \n (7)\n \n \n
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\n \n\n \n \n Ahn, Y., & Lin, Y.\n\n\n \n \n \n \n \n PolicyFlow: Interpreting Policy Diffusion in Context.\n \n \n \n \n\n\n \n\n\n\n ACM Transactions on Interactive Intelligent Systems, 10(2): 13:1–13:23. 2020.\n \n\n(Best Paper Award of The Year)\n\n
\n\n\n\n \n \n \"PolicyFlow:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{ahnPolicyFlowInterpretingPolicy2020,\n  title = {{{PolicyFlow}}: {{Interpreting Policy Diffusion}} in {{Context}}},\n  shorttitle = {{{PolicyFlow}}},\n  author = {Ahn, Yongsu and Lin, Yu-Ru},\n  year = {2020},\n  journal = {ACM Transactions on Interactive Intelligent Systems},\n  volume = {10},\n  number = {2},\n  pages = {13:1--13:23},\n  issn = {2160-6455},\n  doi = {10.1145/3385729},\n  url = {https://bit.ly/policyflow},\n  urldate = {2020-06-19},\n  abstract = {Stability in social, technical, and financial systems, as well as the capacity of organizations to work across borders, requires consistency in public policy across jurisdictions. The diffusion of laws and regulations across political boundaries can reduce the tension that arises between innovation and consistency. Policy diffusion has been a topic of focus across the social sciences for several decades, but due to limitations of data and computational capacity, researchers have not taken a comprehensive and data-intensive look at the aggregate, cross-policy patterns of diffusion. This work combines visual analytics and text and network analyses to help understand how policies, as represented in digitized text, spread across states. As a result, our approach can quickly guide analysts to progressively gain insights into policy adoption data. We evaluate the effectiveness of our system via case studies with a real-world policy dataset and qualitative interviews with domain experts.},\n  bibbase_note = {(Best Paper Award of The Year)},\n}\n\n
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\n Stability in social, technical, and financial systems, as well as the capacity of organizations to work across borders, requires consistency in public policy across jurisdictions. The diffusion of laws and regulations across political boundaries can reduce the tension that arises between innovation and consistency. Policy diffusion has been a topic of focus across the social sciences for several decades, but due to limitations of data and computational capacity, researchers have not taken a comprehensive and data-intensive look at the aggregate, cross-policy patterns of diffusion. This work combines visual analytics and text and network analyses to help understand how policies, as represented in digitized text, spread across states. As a result, our approach can quickly guide analysts to progressively gain insights into policy adoption data. We evaluate the effectiveness of our system via case studies with a real-world policy dataset and qualitative interviews with domain experts.\n
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\n \n\n \n \n Lin, Y., & Chung, W.\n\n\n \n \n \n \n \n The Dynamics of Twitter Users' Gun Narratives across Major Mass Shooting Events.\n \n \n \n \n\n\n \n\n\n\n Humanities and Social Sciences Communications, 7(1): 46. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 14 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{linDynamicsTwitterUsers2020,\n  title = {The Dynamics of {{Twitter}} Users' Gun Narratives across Major Mass Shooting Events},\n  author = {Lin, Yu-Ru and Chung, Wen-Ting},\n  year = {2020},\n  journal = {Humanities and Social Sciences Communications},\n  volume = {7},\n  number = {1},\n  pages = {46},\n  issn = {2662-9992},\n  doi = {10.1057/s41599-020-00533-8},\n  url = {https://doi.org/10.1057/s41599-020-00533-8},\n  abstract = {This study reveals a shift of gun-related narratives created by two ideological groups during three high-profile mass shootings in the United States across the years from 2016 to 2018. It utilizes large-scale, longitudinal social media traces from over 155,000 ideology-identifiable Twitter users. The study design leveraged both the linguistic dictionary approach as well as thematic coding inspired by Narrative Policy Framework, which allows for statistical and qualitative comparison. We found several distinctive narrative characteristics between the two ideology groups in response to the shooting events\\textemdash two groups differed by how they incorporated linguistic and narrative features in their tweets in terms of policy stance, attribution (how one believed to be the problem, the cause or blame, and the solution), the rhetoric employed, and emotion throughout the incidents. The findings suggest how shooting events may penetrate the public discursive processes that had been previously dominated by existing ideological references and may facilitate discussions beyond ideological identities. Overall, in the wake of mass shooting events, the tweets adhering to the majority policy stance within a camp declined, whereas the proportion of mixed or flipped stance tweets increased. Meanwhile, more tweets were observed to express causal reasoning of a held policy stance, and a different pattern in the use of rhetoric schemes, such as the decline of provocative ridicule, emerged. The shifting patterns in users' narratives coincide with the two groups distinctive emotional response revealed in text. These findings offer insights into the opportunity to reconcile conflicts and the potential for creating civic technologies to improve the interpretability of linguistic and narrative signals and to support diverse narratives and framing.},\n}\n\n
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\n This study reveals a shift of gun-related narratives created by two ideological groups during three high-profile mass shootings in the United States across the years from 2016 to 2018. It utilizes large-scale, longitudinal social media traces from over 155,000 ideology-identifiable Twitter users. The study design leveraged both the linguistic dictionary approach as well as thematic coding inspired by Narrative Policy Framework, which allows for statistical and qualitative comparison. We found several distinctive narrative characteristics between the two ideology groups in response to the shooting events— two groups differed by how they incorporated linguistic and narrative features in their tweets in terms of policy stance, attribution (how one believed to be the problem, the cause or blame, and the solution), the rhetoric employed, and emotion throughout the incidents. The findings suggest how shooting events may penetrate the public discursive processes that had been previously dominated by existing ideological references and may facilitate discussions beyond ideological identities. Overall, in the wake of mass shooting events, the tweets adhering to the majority policy stance within a camp declined, whereas the proportion of mixed or flipped stance tweets increased. Meanwhile, more tweets were observed to express causal reasoning of a held policy stance, and a different pattern in the use of rhetoric schemes, such as the decline of provocative ridicule, emerged. The shifting patterns in users' narratives coincide with the two groups distinctive emotional response revealed in text. These findings offer insights into the opportunity to reconcile conflicts and the potential for creating civic technologies to improve the interpretability of linguistic and narrative signals and to support diverse narratives and framing.\n
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\n \n\n \n \n Zhang, J., Wang, W., Xia, F., Lin, Y., & Tong, H.\n\n\n \n \n \n \n \n Data-Driven Computational Social Science: A Survey.\n \n \n \n \n\n\n \n\n\n\n Big Data Research,100145. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"Data-DrivenPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{zhangDatadrivenComputationalSocial2020,\n  title = {Data-Driven {{Computational Social Science}}: {{A Survey}}},\n  shorttitle = {Data-Driven {{Computational Social Science}}},\n  author = {Zhang, Jun and Wang, Wei and Xia, Feng and Lin, Yu-Ru and Tong, Hanghang},\n  year = {2020},\n  journal = {Big Data Research},\n  pages = {100145},\n  publisher = {{Elsevier}},\n  doi = {10.1016/j.bdr.2020.100145},\n  url = {https://arxiv.org/pdf/2008.12372.pdf}\n}\n\n
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\n \n\n \n \n Guo, M., Hwa, R., Lin, Y., & Chung, W.\n\n\n \n \n \n \n \n Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of The 28th International Conference on Computational Linguistics (COLING-2020), 2020. \n \n\n\n\n
\n\n\n\n \n \n \"InflatingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{guoInflatingTopicRelevance2020,\n  title = {Inflating {{Topic Relevance}} with {{Ideology}}: {{A Case Study}} of {{Political Ideology Bias}} in {{Social Topic Detection Models}}},\n  booktitle = {Proceedings of {{The}} 28th {{International Conference}} on {{Computational Linguistics}} ({{COLING-2020}})},\n  author = {Guo, Meiqi and Hwa, Rebecca and Lin, Yu-Ru and Chung, Wen-Ting},\n  year = {2020},\n  url = {http://bit.ly/bias-inflating},\n}\n\n
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\n \n\n \n \n Li, A., Wang, A., Nazari, Z., Chandar, P., & Carterette, B.\n\n\n \n \n \n \n \n Do Podcasts and Music Compete with One Another? Understanding Users’ Audio Streaming Habits.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of The Web Conference 2020, of WWW '20, pages 1920–1931, New York, NY, USA, 2020. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"DoPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{liPodcastsMusicCompete2020,\n  title = {Do Podcasts and Music Compete with One Another? {{Understanding}} Users\\&\\#x2019; Audio Streaming Habits},\n  shorttitle = {Do Podcasts and Music Compete with One Another?},\n  booktitle = {Proceedings of {{The Web Conference}} 2020},\n  author = {Li, Ang and Wang, Alice and Nazari, Zahra and Chandar, Praveen and Carterette, Benjamin},\n  year = {2020},\n  series = {{{WWW}} '20},\n  pages = {1920--1931},\n  publisher = {{Association for Computing Machinery}},\n  address = {{New York, NY, USA}},\n  doi = {10.1145/3366423.3380260},\n  url = {https://doi.org/10.1145/3366423.3380260},\n  urldate = {2021-12-22},\n  abstract = {Over the past decade, podcasts have been one of the fastest growing online streaming media. Many online audio streaming platforms such as Pandora, Spotify, etc. that traditionally focused on music content have started to incorporate services related to podcasts. Although incorporating new media types such as podcasts has created tremendous opportunities for these streaming platforms to expand their content offering, it also introduces new challenges. Since the functional use of podcasts and music may largely overlap for many people, the two types of content may compete with one another for the finite amount of time that users may allocate for audio streaming. As a result, incorporating podcast listening may influence and change the way users have originally consumed music. Adopting quasi-experimental techniques, the current study assesses the causal influence of adding a new class of content on user listening behavior by using large scale observational data collected from a widely used audio streaming platform. Our results demonstrate that podcast and music consumption compete slightly but do not replace one another \\textendash{} users open another time window to listen to podcasts. In addition, users who have added podcasts to their music listening demonstrate significantly different consumption habits for podcasts vs. music in terms of the streaming time, duration and frequency. Taking all the differences as input features to a machine learning model, we demonstrate that a podcast listening session is predictable at the start of a new listening session. Our study provides a novel contribution for online audio streaming and consumption services to understand their potential consumers and to best support their current users with an improved recommendation system.},\n  isbn = {978-1-4503-7023-3}\n}\n\n
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\n Over the past decade, podcasts have been one of the fastest growing online streaming media. Many online audio streaming platforms such as Pandora, Spotify, etc. that traditionally focused on music content have started to incorporate services related to podcasts. Although incorporating new media types such as podcasts has created tremendous opportunities for these streaming platforms to expand their content offering, it also introduces new challenges. Since the functional use of podcasts and music may largely overlap for many people, the two types of content may compete with one another for the finite amount of time that users may allocate for audio streaming. As a result, incorporating podcast listening may influence and change the way users have originally consumed music. Adopting quasi-experimental techniques, the current study assesses the causal influence of adding a new class of content on user listening behavior by using large scale observational data collected from a widely used audio streaming platform. Our results demonstrate that podcast and music consumption compete slightly but do not replace one another – users open another time window to listen to podcasts. In addition, users who have added podcasts to their music listening demonstrate significantly different consumption habits for podcasts vs. music in terms of the streaming time, duration and frequency. Taking all the differences as input features to a machine learning model, we demonstrate that a podcast listening session is predictable at the start of a new listening session. Our study provides a novel contribution for online audio streaming and consumption services to understand their potential consumers and to best support their current users with an improved recommendation system.\n
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\n \n\n \n \n Wei, K., Lin, Y., & Yan, M.\n\n\n \n \n \n \n \n Examining Protest as An Intervention to Reduce Online Prejudice: A Case Study of Prejudice Against Immigrants.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of The Web Conference 2020 (WWW 2020), 2020. \n \n\n\n\n
\n\n\n\n \n \n \"ExaminingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 10 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{weiExaminingProtestIntervention2020,\n  title = {Examining {{Protest}} as {{An Intervention}} to {{Reduce Online Prejudice}}: {{A Case Study}} of {{Prejudice Against Immigrants}}},\n  booktitle = {Proceedings of {{The Web Conference}} 2020 ({{WWW}} 2020)},\n  author = {Wei, Kai and Lin, Yu-Ru and Yan, Muheng},\n  year = {2020},\n  doi = {10.1145/3366423.3380307},\n  url = {http://bit.ly/protest-counter-prejudice},\n}\n\n
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\n \n\n \n \n Yan, M., Lin, Y., Hwa, R., Ertugrul, A. M., Guo, M., & Chung, W.\n\n\n \n \n \n \n \n MimicProp: Learning to Incorporate Lexicon Knowledge into Distributed Word Representation for Social Media Analysis.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the 14th International AAAI Conference on Web and Social Media (ICWSM 2020), 2020. AAAI\n \n\n\n\n
\n\n\n\n \n \n \"MimicProp:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{yanMimicPropLearningIncorporate2020,\n  title = {{{MimicProp}}: {{Learning}} to {{Incorporate Lexicon Knowledge}} into {{Distributed Word Representation}} for {{Social Media Analysis}}},\n  booktitle = {Proceedings of the 14th {{International AAAI Conference}} on {{Web}} and {{Social Media}} ({{ICWSM}} 2020)},\n  author = {Yan, Muheng and Lin, Yu-Ru and Hwa, Rebecca and Ertugrul, Ali Mert and Guo, Meiqi and Chung, Wen-Ting},\n  year = {2020},\n  publisher = {{AAAI}},\n  url = {http://bitly.com/mimicprop},\n}\n\n
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\n  \n 2019\n \n \n (8)\n \n \n
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\n \n\n \n \n Ahn, Y., & Lin, Y.\n\n\n \n \n \n \n \n FairSight: Visual Analytics for Fairness in Decision Making.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Visualization and Computer Graphics (TVCG),1–1. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"FairSight:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{ahnFairSightVisualAnalytics2019,\n  title = {{{FairSight}}: {{Visual Analytics}} for {{Fairness}} in {{Decision Making}}},\n  shorttitle = {{{FairSight}}},\n  author = {Ahn, Yongsu and Lin, Yu-Ru},\n  year = {2019},\n  journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)},\n  pages = {1--1},\n  issn = {1077-2626},\n  doi = {10.1109/TVCG.2019.2934262},\n  url = {https://arxiv.org/pdf/1908.00176.pdf},\n  abstract = {Data-driven decision making related to individuals has become increasingly pervasive, but the issue concerning the potential discrimination has been raised by recent studies. In response, researchers have made efforts to propose and implement fairness measures and algorithms, but those efforts have not been translated to the real-world practice of data-driven decision making. As such, there is still an urgent need to create a viable tool to facilitate fair decision making. We propose FairSight, a visual analytic system to address this need; it is designed to achieve different notions of fairness in ranking decisions through identifying the required actions - understanding, measuring, diagnosing and mitigating biases - that together lead to fairer decision making. Through a case study and user study, we demonstrate that the proposed visual analytic and diagnostic modules in the system are effective in understanding the fairness-aware decision pipeline and obtaining more fair outcomes.},\n}\n\n
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\n Data-driven decision making related to individuals has become increasingly pervasive, but the issue concerning the potential discrimination has been raised by recent studies. In response, researchers have made efforts to propose and implement fairness measures and algorithms, but those efforts have not been translated to the real-world practice of data-driven decision making. As such, there is still an urgent need to create a viable tool to facilitate fair decision making. We propose FairSight, a visual analytic system to address this need; it is designed to achieve different notions of fairness in ranking decisions through identifying the required actions - understanding, measuring, diagnosing and mitigating biases - that together lead to fairer decision making. Through a case study and user study, we demonstrate that the proposed visual analytic and diagnostic modules in the system are effective in understanding the fairness-aware decision pipeline and obtaining more fair outcomes.\n
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\n \n\n \n \n Ertugrul, A. M., Lin, Y., Chung, W., Yan, M., & Li, A.\n\n\n \n \n \n \n \n Activism via Attention: Interpretable Spatiotemporal Learning to Forecast Protest Activities.\n \n \n \n \n\n\n \n\n\n\n EPJ Data Science. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"ActivismPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{ertugrulActivismAttentionInterpretable2019,\n  title = {Activism via {{Attention}}: {{Interpretable Spatiotemporal Learning}} to {{Forecast Protest Activities}}},\n  author = {Ertugrul, Ali Mert and Lin, Yu-Ru and Chung, Wen-Ting and Yan, Muheng and Li, Ang},\n  year = {2019},\n  journal = {EPJ Data Science},\n  doi = {10.1140/epjds/s13688-019-0183-y},\n  url = {https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-019-0183-y},\n}\n\n
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\n \n\n \n \n Hajiseyedjavadi, S., Lin, Y., & Pelechrinis, K.\n\n\n \n \n \n \n \n Learning Embeddings for Multiplex Networks Using Triplet Loss.\n \n \n \n \n\n\n \n\n\n\n Applied Network Science, 4(1): 125. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"LearningPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{hajiseyedjavadiLearningEmbeddingsMultiplex2019,\n  title = {Learning Embeddings for Multiplex Networks Using Triplet Loss},\n  author = {Hajiseyedjavadi, Seyedsaeed and Lin, Yu-Ru and Pelechrinis, Konstantinos},\n  year = {2019},\n  journal = {Applied Network Science},\n  volume = {4},\n  number = {1},\n  pages = {125},\n  publisher = {{Springer}},\n  doi = {10.1007/s41109-019-0242-0},\n  url = {https://link.springer.com/content/pdf/10.1007/s41109-019-0242-0.pdf},\n}\n\n
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\n \n\n \n \n Wormwood, J. B., Lin, Y., Lynn, S. K., Barrett, L. F., & Quigley, K. S.\n\n\n \n \n \n \n \n Psychological Impact of Mass Violence Depends on Affective Tone of Media Content.\n \n \n \n \n\n\n \n\n\n\n PLoS One, 14(4): e0213891. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"PsychologicalPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wormwoodPsychologicalImpactMass2019,\n  title = {Psychological Impact of Mass Violence Depends on Affective Tone of Media Content},\n  author = {Wormwood, Jolie Baumann and Lin, Yu-Ru and Lynn, Spencer K. and Barrett, Lisa Feldman and Quigley, Karen S.},\n  year = {2019},\n  journal = {PLoS One},\n  volume = {14},\n  number = {4},\n  pages = {e0213891},\n  doi = {10.1371/journal.pone.0213891},\n  url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0213891}\n}\n\n
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\n \n\n \n \n Ertugrul, A. M., Lin, Y., & Temizel, T. T.\n\n\n \n \n \n \n \n CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2019), 2019. \n \n\n\n\n
\n\n\n\n \n \n \"CASTNet:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{ertugrulCASTNetCommunityAttentiveSpatioTemporal2019,\n  title = {{{CASTNet}}: {{Community-Attentive Spatio-Temporal Networks}} for {{Opioid Overdose Forecasting}}},\n  booktitle = {Proceedings of {{Joint European Conference}} on {{Machine Learning}} and {{Knowledge Discovery}} in {{Databases}} ({{ECML PKDD}} 2019)},\n  author = {Ertugrul, Ali Mert and Lin, Yu-Ru and Temizel, Tugba Taskaya},\n  year = {2019},\n  url = {http://bit.ly/community-attentive},\n}\n\n
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\n \n\n \n \n Li, A., Thom, J., Chandar, P., Hosey, C., Thomas, B. S., & Garcia-Gathright, J.\n\n\n \n \n \n \n \n Search Mindsets: Understanding Focused and Non-Focused Information Seeking in Music Search.\n \n \n \n \n\n\n \n\n\n\n In The World Wide Web Conference, of WWW '19, pages 2971–2977, New York, NY, USA, 2019. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"SearchPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{liSearchMindsetsUnderstanding2019,\n  title = {Search {{Mindsets}}: {{Understanding Focused}} and {{Non-Focused Information Seeking}} in {{Music Search}}},\n  shorttitle = {Search {{Mindsets}}},\n  booktitle = {The {{World Wide Web Conference}}},\n  author = {Li, Ang and Thom, Jennifer and Chandar, Praveen and Hosey, Christine and Thomas, Brian St. and {Garcia-Gathright}, Jean},\n  year = {2019},\n  series = {{{WWW}} '19},\n  pages = {2971--2977},\n  publisher = {{Association for Computing Machinery}},\n  address = {{New York, NY, USA}},\n  doi = {10.1145/3308558.3313627},\n  url = {https://doi.org/10.1145/3308558.3313627},\n  urldate = {2021-12-22},\n  abstract = {Music listening is a commonplace activity that has transformed as users engage with online streaming platforms. When presented with anytime, anywhere access to a vast catalog of music, users face challenges in searching for what they want to hear. We propose that users who engage in domain-specific search (e.g., music search) have different information-seeking needs than in general search. Using a mixed-method approach that combines a large-scale user survey with behavior data analyses, we describe the construct of search mindset on a leading online streaming music platform and then investigate two types of search mindsets: focused, where a user is looking for one thing in particular, and non-focused, where a user is open to different results. Our results reveal that searches in the music domain are more likely to be focused than non-focused. In addition, users' behavior (e.g., clicks, streams, querying, etc.) on a music search system is influenced by their search mindset. Finally, we propose design implications for music search systems to best support their users.},\n  isbn = {978-1-4503-6674-8}\n}\n\n
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\n Music listening is a commonplace activity that has transformed as users engage with online streaming platforms. When presented with anytime, anywhere access to a vast catalog of music, users face challenges in searching for what they want to hear. We propose that users who engage in domain-specific search (e.g., music search) have different information-seeking needs than in general search. Using a mixed-method approach that combines a large-scale user survey with behavior data analyses, we describe the construct of search mindset on a leading online streaming music platform and then investigate two types of search mindsets: focused, where a user is looking for one thing in particular, and non-focused, where a user is open to different results. Our results reveal that searches in the music domain are more likely to be focused than non-focused. In addition, users' behavior (e.g., clicks, streams, querying, etc.) on a music search system is influenced by their search mindset. Finally, we propose design implications for music search systems to best support their users.\n
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\n \n\n \n \n Wen, X., Lin, Y., Ahn, Y., Pelechrinis, K., Liu, X., & Cao, N.\n\n\n \n \n \n \n FacIt: Factorizing Tensors into Interpretable and Scrutinizable Patterns.\n \n \n \n\n\n \n\n\n\n In IEEE Symposium on Visual Analytics Science and Technology (VAST 2019), 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wenFacItFactorizingTensors2019,\n  title = {{{FacIt}}: {{Factorizing Tensors}} into {{Interpretable}} and {{Scrutinizable Patterns}}},\n  booktitle = {{{IEEE Symposium}} on {{Visual Analytics Science}} and {{Technology}} ({{VAST}} 2019)},\n  author = {Wen, Xidao and Lin, Yu-Ru and Ahn, Yongsu and Pelechrinis, Konstantinos and Liu, Xi and Cao, Nan},\n  year = {2019},\n}\n\n
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\n \n\n \n \n Wen, X., Lin, Y., Liu, X., Brusilovsky, P., & Barria Pineda, J.\n\n\n \n \n \n \n \n Iterative Discriminant Tensor Factorization for Behavior Comparison in Massive Open Online Courses.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of The Web Conference 2019 (WWW 2019), 2019. \n \n\n\n\n
\n\n\n\n \n \n \"IterativePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wenIterativeDiscriminantTensor2019,\n  title = {Iterative {{Discriminant Tensor Factorization}} for {{Behavior Comparison}} in {{Massive Open Online Courses}}},\n  booktitle = {Proceedings of {{The Web Conference}} 2019 ({{WWW}} 2019)},\n  author = {Wen, Xidao and Lin, Yu-Ru and Liu, Xi and Brusilovsky, Peter and Barria Pineda, Jordan},\n  year = {2019},\n  doi = {10.1145/3308558.3313713},\n  url = {http://d-scholarship.pitt.edu/36851/3/p2068-wen.pdf},\n}\n\n
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\n  \n 2018\n \n \n (10)\n \n \n
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\n \n\n \n \n Booth, J., Lin, Y., & Wei, K.\n\n\n \n \n \n \n Neighborhood Disadvantage, Residents' Distress, and Online Social Communication: Harnessing Twitter Data to Examine Neighborhood Effects.\n \n \n \n\n\n \n\n\n\n Journal of Community Psychology (JCOP), 46(7): 829–843. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{boothNeighborhoodDisadvantageResidents2018,\n  title = {Neighborhood {{Disadvantage}}, {{Residents}}' {{Distress}}, and {{Online Social Communication}}: {{Harnessing Twitter Data}} to {{Examine Neighborhood Effects}}},\n  author = {Booth, Jaime and Lin, Yu-Ru and Wei, Kai},\n  year = {2018},\n  journal = {Journal of Community Psychology (JCOP)},\n  volume = {46},\n  number = {7},\n  pages = {829--843},\n  doi = {10.1002/jcop.21975}\n}\n\n
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\n \n\n \n \n Cao, N., Lin, C., Zhu, Q., Lin, Y., Teng, X., & Wen, X.\n\n\n \n \n \n \n \n Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Visualization and Computer Graphics (TVCG), 24(1): 23–33. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"Voila:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{caoVoilaVisualAnomaly2018,\n  title = {Voila: {{Visual Anomaly Detection}} and {{Monitoring}} with {{Streaming Spatiotemporal Data}}},\n  author = {Cao, Nan and Lin, Chaoguang and Zhu, Qiuhan and Lin, Yu-Ru and Teng, Xian and Wen, Xidao},\n  year = {2018},\n  journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)},\n  volume = {24},\n  number = {1},\n  pages = {23--33},\n  doi = {10.1109/TVCG.2017.2744419},\n  url = {http://bit.ly/voila-paper},\n}\n\n
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\n \n\n \n \n Lin, C., Zhu, Q., Guo, S., Jin, Z., Lin, Y., & Cao, N.\n\n\n \n \n \n \n \n Anomaly Detection in Spatial-temporal Data via Regularized Non-Negative Tensor Analysis.\n \n \n \n \n\n\n \n\n\n\n Data Mining and Knowledge Discovery (DAMI), 32(4): 1056–1073. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"AnomalyPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{linAnomalyDetectionSpatialtemporal2018,\n  title = {Anomaly {{Detection}} in {{Spatial-temporal Data}} via {{Regularized Non-Negative Tensor Analysis}}},\n  author = {Lin, Chaoguang and Zhu, Qiuhan and Guo, Shunan and Jin, Zhuochen and Lin, Yu-Ru and Cao, Nan},\n  year = {2018},\n  journal = {Data Mining and Knowledge Discovery (DAMI)},\n  volume = {32},\n  number = {4},\n  pages = {1056--1073},\n  issn = {1384-5810},\n  doi = {10.1007/s10618-018-0560-3},\n  url = {https://link.springer.com/article/10.1007/s10618-018-0560-3},\n}\n\n
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\n \n\n \n \n Wen, X., Lin, Y., & Pelechrinis, K.\n\n\n \n \n \n \n \n Event Analytics via Discriminant Tensor Factorization.\n \n \n \n \n\n\n \n\n\n\n ACM Transactions on Knowledge Discovery from Data (TKDD), 12(6): 72. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"EventPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wenEventAnalyticsDiscriminant2018,\n  title = {Event {{Analytics}} via {{Discriminant Tensor Factorization}}},\n  author = {Wen, Xidao and Lin, Yu-Ru and Pelechrinis, Konstantinos},\n  year = {2018},\n  journal = {ACM Transactions on Knowledge Discovery from Data (TKDD)},\n  volume = {12},\n  number = {6},\n  pages = {72},\n  doi = {10.1145/3184455},\n  url = {http://bit.ly/event-tensor-fac},\n}\n\n
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\n \n\n \n \n Ahn, Y., & Lin, Y.\n\n\n \n \n \n \n PolicyFlow: Interpreting Policy Diffusion in Context.\n \n \n \n\n\n \n\n\n\n In KDD 2018 Workshop on Interactive Data Exploration and Analytics (IDEA 2018), volume 46, pages 829–843, 2018. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{ahnPolicyFlowInterpretingPolicy2018,\n  title = {{PolicyFlow: Interpreting Policy Diffusion in Context}},\n  booktitle = {{KDD 2018 Workshop on Interactive Data Exploration and Analytics (IDEA 2018)}},\n  author = {Ahn, Yongsu and Lin, Yu-Ru},\n  year = {2018},\n  volume = {46},\n  pages = {829--843},\n}\n\n
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\n \n\n \n \n Chung, W., Lin, Y., Li, A., Ertugrul, A. M., & Yan, M.\n\n\n \n \n \n \n \n March with and without Feet: The Talking about Protests and Beyond.\n \n \n \n \n\n\n \n\n\n\n In Proc. of the 10th International Conference on Social Informatics (SocInfo 2018), 2018. \n \n\n\n\n
\n\n\n\n \n \n \"MarchPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{chungMarchFeetTalking2018,\n  title = {March with and without {{Feet}}: {{The Talking}} about {{Protests}} and {{Beyond}}},\n  booktitle = {Proc. of the 10th {{International Conference}} on {{Social Informatics}} ({{SocInfo}} 2018)},\n  author = {Chung, Wen-Ting and Lin, Yu-Ru and Li, Ang and Ertugrul, Ali Mert and Yan, Muheng},\n  year = {2018},\n  doi = {10.1007/978-3-030-01129-1_9},\n  url = {http://bit.ly/march-without-feet},\n}\n\n
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\n \n\n \n \n Ertugrul, A. M., Lin, Y., Mair, C., & Temizel, T. T.\n\n\n \n \n \n \n \n Forecasting Heroin Overdose Occurrences from Crime Incidents.\n \n \n \n \n\n\n \n\n\n\n In 2018 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2018), 2018. \n \n\n\n\n
\n\n\n\n \n \n \"ForecastingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{ertugrulForecastingHeroinOverdose2018,\n  title = {Forecasting {{Heroin Overdose Occurrences}} from {{Crime Incidents}}},\n  booktitle = {2018 {{International Conference}} on {{Social Computing}}, {{Behavioral-Cultural Modeling}} \\& {{Prediction}} and {{Behavior Representation}} in {{Modeling}} and {{Simulation}} ({{SBP-BRiMS}} 2018)},\n  author = {Ertugrul, Ali Mert and Lin, Yu-Ru and Mair, Christina and Temizel, Tugba Taskaya},\n  year = {2018},\n  url = {http://sbp-brims.org/2018/proceedings/papers/challenge_papers/SBP-BRiMS_2018_paper_122.pdf},\n}\n\n
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\n \n\n \n \n Javadi, S., Lin, Y., & Pelechrinis, K.\n\n\n \n \n \n \n Discovering Functionality of Urban Regions by Learning Low-dimensional Representations of a Spatial Multiplex Network.\n \n \n \n\n\n \n\n\n\n In KDD 2018 Mining Urban Data (MUD) Workshop, 2018. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{javadiDiscoveringFunctionalityUrban2018,\n  title = {{Discovering Functionality of Urban Regions by Learning Low-dimensional Representations of a Spatial Multiplex Network}},\n  booktitle = {{KDD 2018 Mining Urban Data (MUD) Workshop}},\n  author = {Javadi, Saeed and Lin, Yu-Ru and Pelechrinis, Konstantinos},\n  year = {2018},\n}\n\n
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\n \n\n \n \n Liu, X., Xie, M., Wen, X., Chen, R., Ge, Y., Duffield, N., & Wang, N.\n\n\n \n \n \n \n A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games.\n \n \n \n\n\n \n\n\n\n In 2018 IEEE International Conference on Data Mining (ICDM), pages 277–286, 2018. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{liuSemiSupervisedInductiveEmbedding2018,\n  title = {A {{Semi-Supervised}} and {{Inductive Embedding Model}} for {{Churn Prediction}} of {{Large-Scale Mobile Games}}},\n  booktitle = {2018 {{IEEE International Conference}} on {{Data Mining}} ({{ICDM}})},\n  author = {Liu, Xi and Xie, Muhe and Wen, Xidao and Chen, Rui and Ge, Yong and Duffield, Nick and Wang, Na},\n  year = {2018},\n  pages = {277--286},\n  issn = {2374-8486},\n  doi = {10.1109/ICDM.2018.00043},\n  abstract = {Mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. One critical challenge for these platforms and services is to understand user churn behavior in mobile games. Accurate churn prediction will benefit many stakeholders such as game developers, advertisers, and platform operators. In this paper, we present the first large-scale churn prediction solution for mobile games. In view of the common limitations of the state-of-the-art methods built upon traditional machine learning models, we devise a novel semi-supervised and inductive embedding model that jointly learns the prediction function and the embedding function for user-app relationships. We model these two functions by deep neural networks with a unique edge embedding technique that is able to capture both contextual information and relationship dynamics. We also design a novel attributed random walk technique that takes into consideration both topological adjacency and attribute similarities. To evaluate the performance of our solution, we collect real-world data from the Samsung Game Launcher platform that includes tens of thousands of games and hundreds of millions of user-app interactions. The experimental results with this data demonstrate the superiority of our proposed model against existing state-of-the-art methods.}\n}\n\n
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\n Mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. One critical challenge for these platforms and services is to understand user churn behavior in mobile games. Accurate churn prediction will benefit many stakeholders such as game developers, advertisers, and platform operators. In this paper, we present the first large-scale churn prediction solution for mobile games. In view of the common limitations of the state-of-the-art methods built upon traditional machine learning models, we devise a novel semi-supervised and inductive embedding model that jointly learns the prediction function and the embedding function for user-app relationships. We model these two functions by deep neural networks with a unique edge embedding technique that is able to capture both contextual information and relationship dynamics. We also design a novel attributed random walk technique that takes into consideration both topological adjacency and attribute similarities. To evaluate the performance of our solution, we collect real-world data from the Samsung Game Launcher platform that includes tens of thousands of games and hundreds of millions of user-app interactions. The experimental results with this data demonstrate the superiority of our proposed model against existing state-of-the-art methods.\n
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\n \n\n \n \n Teng, X., Yan, M., Ertugrul, A. M., & Lin, Y.\n\n\n \n \n \n \n \n Deep into Hypersphere: Robust and Unsupervised Anomaly Discovery in Dynamic Networks.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), 2018. \n \n\n\n\n
\n\n\n\n \n \n \"DeepPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{tengDeepHypersphereRobust2018,\n  title = {Deep into {{Hypersphere}}: {{Robust}} and {{Unsupervised Anomaly Discovery}} in {{Dynamic Networks}}},\n  booktitle = {Proceedings of the 27th {{International Joint Conference}} on {{Artificial Intelligence}} ({{IJCAI}} 2018)},\n  author = {Teng, Xian and Yan, Muheng and Ertugrul, Ali Mert and Lin, Yu-Ru},\n  year = {2018},\n  url = {http://goo.gl/e3aons},\n}\n\n
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\n  \n 2017\n \n \n (16)\n \n \n
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\n \n\n \n \n Pei, S., Teng, X., Shaman, J., Morone, F., & Makse, H. A\n\n\n \n \n \n \n Efficient Collective Influence Maximization in Cascading Processes with First-Order Transitions.\n \n \n \n\n\n \n\n\n\n Scientific Reports, 7(1): 1–13. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{pei2017efficient,\n  title={Efficient Collective Influence Maximization in Cascading Processes with First-Order Transitions},\n  author={Pei, Sen and Teng, Xian and Shaman, Jeffrey and Morone, Flaviano and Makse, Hern{\\'a}n A},\n  journal={Scientific Reports},\n  volume={7},\n  number={1},\n  pages={1--13},\n  year={2017},\n  doi={https://doi.org/10.1038/srep45240},\n  abstract={In many social and biological networks, the collective dynamics of the entire system can be shaped by a small set of influential units through a global cascading process, manifested by an abrupt first-order transition in dynamical behaviors. Despite its importance in applications, efficient identification of multiple influential spreaders in cascading processes still remains a challenging task for large-scale networks. Here we address this issue by exploring the collective influence in general threshold models of cascading process. Our analysis reveals that the importance of spreaders is fixed by the subcritical paths along which cascades propagate: the number of subcritical paths attached to each spreader determines its contribution to global cascades. The concept of subcritical path allows us to introduce a scalable algorithm for massively large-scale networks. Results in both synthetic random graphs and real networks show that the proposed method can achieve larger collective influence given the same number of seeds compared with other scalable heuristic approaches.}\n}\n\n
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\n In many social and biological networks, the collective dynamics of the entire system can be shaped by a small set of influential units through a global cascading process, manifested by an abrupt first-order transition in dynamical behaviors. Despite its importance in applications, efficient identification of multiple influential spreaders in cascading processes still remains a challenging task for large-scale networks. Here we address this issue by exploring the collective influence in general threshold models of cascading process. Our analysis reveals that the importance of spreaders is fixed by the subcritical paths along which cascades propagate: the number of subcritical paths attached to each spreader determines its contribution to global cascades. The concept of subcritical path allows us to introduce a scalable algorithm for massively large-scale networks. Results in both synthetic random graphs and real networks show that the proposed method can achieve larger collective influence given the same number of seeds compared with other scalable heuristic approaches.\n
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\n \n\n \n \n Cao, N., Lin, Y., Gotz, D., & Du, F.\n\n\n \n \n \n \n Z-Glyph: Visualizing Outliers in Multivariate Data.\n \n \n \n\n\n \n\n\n\n Information Visualization. 2017.\n \n\n\n\n
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@article{caoZGlyphVisualizingOutliers2017,\n  title = {Z-{{Glyph}}: {{Visualizing Outliers}} in {{Multivariate Data}}},\n  author = {Cao, Nan and Lin, Yu-Ru and Gotz, David and Du, Fan},\n  year = {2017},\n  journal = {Information Visualization},\n  doi = {10.1177/1473871616686635},\n}\n\n
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\n \n\n \n \n He, X., & Lin, Y.\n\n\n \n \n \n \n \n Measuring and Monitoring Collective Attention During Shocking Events.\n \n \n \n \n\n\n \n\n\n\n EPJ Data Science, 6(30). 2017.\n \n\n\n\n
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@article{heMeasuringMonitoringCollective2017,\n  title = {Measuring and {{Monitoring Collective Attention During Shocking Events}}},\n  author = {He, Xingsheng and Lin, Yu-Ru},\n  year = {2017},\n  journal = {EPJ Data Science},\n  volume = {6},\n  number = {30},\n  doi = {10.1140/epjds/s13688-017-0126-4},\n  url = {http://rdcu.be/zcPU},\n  urldate = {2015-01-05},\n}\n\n
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\n \n\n \n \n Li, L., Tong, H., Cao, N., Ehrlich, K., Lin, Y., & Buchler, N.\n\n\n \n \n \n \n Enhancing Team Composition in Professional Networks: Problem Definitions and Fast Solutions.\n \n \n \n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE), 29(3): 613–626. 2017.\n \n\n\n\n
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@article{liEnhancingTeamComposition2017,\n  title = {Enhancing {{Team Composition}} in {{Professional Networks}}: {{Problem Definitions}} and {{Fast Solutions}}},\n  author = {Li, Liangyue and Tong, Hanghang and Cao, Nan and Ehrlich, Kate and Lin, Yu-Ru and Buchler, Norbou},\n  year = {2017},\n  journal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n  volume = {29},\n  number = {3},\n  pages = {613--626},\n  issn = {1041-4347},\n  doi = {10.1109/TKDE.2016.2633464},\n}\n\n
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\n \n\n \n \n Lin, Y., Kennedy, R., & Lazer, D.\n\n\n \n \n \n \n \n The Geography of Money and Politics: Population Density, Social Networking and Political Contributions.\n \n \n \n \n\n\n \n\n\n\n Research & Politics, 4(4). 2017.\n \n\n\n\n
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@article{linGeographyMoneyPolitics2017,\n  title = {The {{Geography}} of {{Money}} and {{Politics}}: {{Population Density}}, {{Social Networking}} and {{Political Contributions}}},\n  author = {Lin, Yu-Ru and Kennedy, Ryan and Lazer, David},\n  year = {2017},\n  journal = {Research \\& Politics},\n  volume = {4},\n  number = {4},\n  doi = {10.1177/2053168017742015},\n  url = {http://journals.sagepub.com/eprint/z5Bh7RZvUYViqxgw8RMC/full},\n}\n\n
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\n \n\n \n \n Lin, Y., Margolin, D., & Wen, X.\n\n\n \n \n \n \n \n Tracking and Analyzing Individual Distress Following Terrorist Attacks Using Social Media Streams.\n \n \n \n \n\n\n \n\n\n\n Risk Analysis, 37(8): 1580–1605. 2017.\n \n\n\n\n
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@article{linTrackingAnalyzingIndividual2017,\n  title = {Tracking and {{Analyzing Individual Distress Following Terrorist Attacks Using Social Media Streams}}},\n  author = {Lin, Yu-Ru and Margolin, Drew and Wen, Xidao},\n  year = {2017},\n  journal = {Risk Analysis},\n  volume = {37},\n  number = {8},\n  pages = {1580--1605},\n  doi = {10.1111/risa.12829},\n  url = {http://goo.gl/RNVRmJ},\n}\n\n
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\n \n\n \n \n Lopez, C., Farzan, R., & Lin, Y.\n\n\n \n \n \n \n \n Behind the Myths of Citizen Participation: Identifying Sustainability Factors of Hyper-Local Information Systems.\n \n \n \n \n\n\n \n\n\n\n ACM Transactions on Internet Technology (TOIT), 18(1). 2017.\n \n\n\n\n
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@article{lopezMythsCitizenParticipation2017,\n  title = {Behind the Myths of Citizen Participation: {{Identifying}} Sustainability Factors of Hyper-Local Information Systems},\n  author = {Lopez, Claudia and Farzan, Rosta and Lin, Yu-Ru},\n  year = {2017},\n  journal = {ACM Transactions on Internet Technology (TOIT)},\n  volume = {18},\n  number = {1},\n  doi = {10.1145/3093892},\n  url = {http://bit.ly/behind-myths-citizen},\n}\n\n
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\n \n\n \n \n Pelechrinis, K., & Lin, Y.\n\n\n \n \n \n \n \n Tensor-Based Analysis for Urban Networks.\n \n \n \n \n\n\n \n\n\n\n In Encyclopedia of Social Networks and Mining. Springer, 2017.\n \n\n\n\n
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@incollection{pelechrinisTensorbasedAnalysisUrban2017,\n  title = {Tensor-Based {{Analysis}} for {{Urban Networks}}},\n  booktitle = {Encyclopedia of {{Social Networks}} and {{Mining}}},\n  author = {Pelechrinis, Konstantinos and Lin, Yu-Ru},\n  year = {2017},\n  publisher = {{Springer}},\n  url = {https://link.springer.com/referenceworkentry/10.1007%2F978-1-4614-7163-9_110174-1},\n  isbn = {978-1-4614-7163-9},\n}\n\n
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\n \n\n \n \n Cao, N., Lin, C., Zhu, Q., Lin, Y., Teng, X., & Wen, X.\n\n\n \n \n \n \n \n Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data.\n \n \n \n \n\n\n \n\n\n\n In IEEE Symposium on Visual Analytics Science and Technology (VAST 2017), 2017. \n \n\n\n\n
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@inproceedings{caoVoilaVisualAnomaly2017,\n  title = {Voila: {{Visual Anomaly Detection}} and {{Monitoring}} with {{Streaming Spatiotemporal Data}}},\n  booktitle = {{{IEEE Symposium}} on {{Visual Analytics Science}} and {{Technology}} ({{VAST}} 2017)},\n  author = {Cao, Nan and Lin, Chaoguang and Zhu, Qiuhan and Lin, Yu-Ru and Teng, Xian and Wen, Xidao},\n  year = {2017},\n  url = {http://bit.ly/voila-paper},\n}\n\n
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\n \n\n \n \n Chau, H., Li, A., & Lin, Y.\n\n\n \n \n \n \n Predicting Students Performance Based on Their Reading Behaviors.\n \n \n \n\n\n \n\n\n\n In 2017 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2017), 2017. \n \n\n\n\n
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@inproceedings{chauPredictingStudentsPerformance2017,\n  title = {Predicting {{Students Performance Based}} on {{Their Reading Behaviors}}},\n  booktitle = {2017 {{International Conference}} on {{Social Computing}}, {{Behavioral-Cultural Modeling}} \\& {{Prediction}} and {{Behavior Representation}} in {{Modeling}} and {{Simulation}} ({{SBP-BRiMS}} 2017)},\n  author = {Chau, Hung and Li, Ang and Lin, Yu-Ru},\n  year = {2017},\n}\n\n
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\n \n\n \n \n Ding, T., Deng, J., Li, J., & Lin, Y.\n\n\n \n \n \n \n Sentiment Analysis and Political Party Classification in 2016 U.S. President Debates in Twitter.\n \n \n \n\n\n \n\n\n\n In 2017 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2017), 2017. \n \n\n\n\n
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@inproceedings{dingSentimentAnalysisPolitical2017,\n  title = {Sentiment {{Analysis}} and {{Political Party Classification}} in 2016 {{U}}.{{S}}. {{President Debates}} in {{Twitter}}},\n  booktitle = {2017 {{International Conference}} on {{Social Computing}}, {{Behavioral-Cultural Modeling}} \\& {{Prediction}} and {{Behavior Representation}} in {{Modeling}} and {{Simulation}} ({{SBP-BRiMS}} 2017)},\n  author = {Ding, Tianyu and Deng, Junyi and Li, Jingting and Lin, Yu-Ru},\n  year = {2017},\n}\n\n
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\n \n\n \n \n Du, F., Cao, N., Lin, Y., Xu, P., & Tong, H.\n\n\n \n \n \n \n \n iSphere: Focus+Context Sphere Visualization for Interactive Large Graph Exploration.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2017), 2017. ACM\n \n\n\n\n
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@inproceedings{duISphereFocusContext2017,\n  title = {{{iSphere}}: {{Focus}}+{{Context Sphere Visualization}} for {{Interactive Large Graph Exploration}}},\n  shorttitle = {{{iSphere}}},\n  booktitle = {Proceedings of the {{ACM SIGCHI Conference}} on {{Human Factors}} in {{Computing Systems}} ({{CHI}} 2017)},\n  author = {Du, Fan and Cao, Nan and Lin, Yu-Ru and Xu, Panpan and Tong, Hanghang},\n  year = {2017},\n  publisher = {{ACM}},\n  url = {http://goo.gl/8yS4GH},\n}\n\n
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\n \n\n \n \n He, X., Lu, D., Margolin, D., Wang, M., Idrissi, S. E, & Lin, Y.\n\n\n \n \n \n \n \n The Signals and Noise: Actionable Information in Improvised Social Media Channels During a Disaster.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of Web Science 2017 (WebSci 2017), 2017. \n \n\n\n\n
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@inproceedings{heSignalsNoiseActionable2017,\n  title = {The {{Signals}} and {{Noise}}: {{Actionable Information}} in {{Improvised Social Media Channels During}} a {{Disaster}}},\n  booktitle = {Proceedings of {{Web Science}} 2017 ({{WebSci}} 2017)},\n  author = {He, Xingsheng and Lu, Di and Margolin, Drew and Wang, Mengdi and Idrissi, Salma E and Lin, Yu-Ru},\n  year = {2017},\n  url = {http://goo.gl/qRdibS},\n}\n\n
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\n \n\n \n \n Lopez, C., Farzan, R., & Lin, Y.\n\n\n \n \n \n \n Connecting Neighbors: The Double-Edged Sword of Mobilization Messaging in Hyper-local Online Forums.\n \n \n \n\n\n \n\n\n\n In Proceedings of the 28th ACM Conference on Hypertext and Social Media (Hypertext 2017), of HT '17, 2017. ACM\n \n\n\n\n
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@inproceedings{lopezConnectingNeighborsDoubleEdged2017,\n  title = {Connecting {{Neighbors}}: The {{Double-Edged Sword}} of {{Mobilization Messaging}} in {{Hyper-local Online Forums}}},\n  booktitle = {Proceedings of the 28th {{ACM Conference}} on {{Hypertext}} and {{Social Media}} ({{Hypertext}} 2017)},\n  author = {Lopez, Claudia and Farzan, Rosta and Lin, Yu-Ru},\n  year = {2017},\n  series = {{{HT}} '17},\n  publisher = {{ACM}},\n}\n\n
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\n \n\n \n \n Teng, X., Lin, Y., & Wen, X.\n\n\n \n \n \n \n \n Anomaly Detection in Dynamic Networks Using Multi-view Time-Series Hypersphere Learning.\n \n \n \n \n\n\n \n\n\n\n In Proc. of The 26th ACM International Conference on Information and Knowledge Management (CIKM 2017), 2017. \n \n\n\n\n
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@inproceedings{tengAnomalyDetectionDynamic2017,\n  title = {Anomaly {{Detection}} in {{Dynamic Networks}} Using {{Multi-view Time-Series Hypersphere Learning}}},\n  booktitle = {Proc. of {{The}} 26th {{ACM International Conference}} on {{Information}} and {{Knowledge Management}} ({{CIKM}} 2017)},\n  author = {Teng, Xian and Lin, Yu-Ru and Wen, Xidao},\n  year = {2017},\n  url = {http://goo.gl/n7tRjW},\n}\n\n
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\n \n\n \n \n Yan, M., Wen, X., Lin, Y., & Deng, L.\n\n\n \n \n \n \n \n Quantifying Content Polarization on Twitter.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of 2017 IEEE International Conference on Collaboration and Internet Computing (IEEE CIC 2017), 2017. IEEE\n \n\n(Best Student Paper Award)\n\n
\n\n\n\n \n \n \"QuantifyingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{yanQuantifyingContentPolarization2017,\n  title = {Quantifying {{Content Polarization}} on {{Twitter}}},\n  booktitle = {Proceedings of 2017 {{IEEE International Conference}} on {{Collaboration}} and {{Internet Computing}} ({{IEEE CIC}} 2017)},\n  author = {Yan, Muheng and Wen, Xidao and Lin, Yu-Ru and Deng, Lingjia},\n  year = {2017},\n  publisher = {{IEEE}},\n  url = {http://goo.gl/T6NeZw},\n  bibbase_note = {(Best Student Paper Award)},\n}\n\n
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\n  \n 2016\n \n \n (24)\n \n \n
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\n \n\n \n \n Teng, X., Pei, S., Morone, F., & Makse, H. A\n\n\n \n \n \n \n Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks.\n \n \n \n\n\n \n\n\n\n Scientific Reports, 6(1): 1–11. 2016.\n \n\n\n\n
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@article{teng2016collective,\n  title={Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks},\n  author={Teng, Xian and Pei, Sen and Morone, Flaviano and Makse, Hern{\\'a}n A},\n  journal={Scientific Reports},\n  volume={6},\n  number={1},\n  pages={1--11},\n  year={2016},\n  doi={https://doi.org/10.1038/srep36043},\n  abstract={Identifying the most influential spreaders that maximize information flow is a central question in network theory. Recently, a scalable method called “Collective Influence (CI)” has been put forward through collective influence maximization. In contrast to heuristic methods evaluating nodes’ significance separately, CI method inspects the collective influence of multiple spreaders. Despite that CI applies to the influence maximization problem in percolation model, it is still important to examine its efficacy in realistic information spreading. Here, we examine real-world information flow in various social and scientific platforms including American Physical Society, Facebook, Twitter and LiveJournal. Since empirical data cannot be directly mapped to ideal multi-source spreading, we leverage the behavioral patterns of users extracted from data to construct “virtual” information spreading processes. Our results demonstrate that the set of spreaders selected by CI can induce larger scale of information propagation. Moreover, local measures as the number of connections or citations are not necessarily the deterministic factors of nodes’ importance in realistic information spreading. This result has significance for rankings scientists in scientific networks like the APS, where the commonly used number of citations can be a poor indicator of the collective influence of authors in the community.}\n}\n\n
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\n Identifying the most influential spreaders that maximize information flow is a central question in network theory. Recently, a scalable method called “Collective Influence (CI)” has been put forward through collective influence maximization. In contrast to heuristic methods evaluating nodes’ significance separately, CI method inspects the collective influence of multiple spreaders. Despite that CI applies to the influence maximization problem in percolation model, it is still important to examine its efficacy in realistic information spreading. Here, we examine real-world information flow in various social and scientific platforms including American Physical Society, Facebook, Twitter and LiveJournal. Since empirical data cannot be directly mapped to ideal multi-source spreading, we leverage the behavioral patterns of users extracted from data to construct “virtual” information spreading processes. Our results demonstrate that the set of spreaders selected by CI can induce larger scale of information propagation. Moreover, local measures as the number of connections or citations are not necessarily the deterministic factors of nodes’ importance in realistic information spreading. This result has significance for rankings scientists in scientific networks like the APS, where the commonly used number of citations can be a poor indicator of the collective influence of authors in the community.\n
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\n \n\n \n \n Cao, N., Lin, Y., Du, F., & Wang, D.\n\n\n \n \n \n \n Episogram: Visual Summarization of Egocentric Social Interactions.\n \n \n \n\n\n \n\n\n\n IEEE Computer Graphics and Applications (CG&A), 36(5): 72–81. 2016.\n \n\n\n\n
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@article{caoEpisogramVisualSummarization2016,\n  title = {Episogram: {{Visual Summarization}} of {{Egocentric Social Interactions}}},\n  shorttitle = {Episogram},\n  author = {Cao, Nan and Lin, Yu-Ru and Du, Fan and Wang, Dashun},\n  year = {2016},\n  journal = {IEEE Computer Graphics and Applications (CG\\&A)},\n  volume = {36},\n  number = {5},\n  pages = {72--81},\n  issn = {0272-1716},\n  doi = {10.1109/MCG.2015.73},\n}\n\n
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\n \n\n \n \n Cao, N., Shi, C., Lin, S., Lu, J., Lin, Y., & Lin, C.\n\n\n \n \n \n \n \n TargetVue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Visualization and Computer Graphics (TVCG), 22(1): 280–289. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"TargetVue:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{caoTargetVueVisualAnalysis2016,\n  title = {{{TargetVue}}: {{Visual Analysis}} of {{Anomalous User Behaviors}} in {{Online Communication Systems}}},\n  author = {Cao, Nan and Shi, Conglei and Lin, Sabrina and Lu, Jie and Lin, Yu-Ru and Lin, Ching-Yung},\n  year = {2016},\n  journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)},\n  volume = {22},\n  number = {1},\n  pages = {280--289},\n  issn = {1077-2626},\n  doi = {10.1109/TVCG.2015.2467196},\n  url = {http://bit.ly/targetvue},\n}\n\n\n
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\n \n\n \n \n Lu, Z., Lin, Y., Huang, X., Xiong, N., & Fang, Z.\n\n\n \n \n \n \n Visual Topic Discovering, Tracking and Summarization from Social Media Streams.\n \n \n \n\n\n \n\n\n\n Multimedia Tools and Applications. 2016.\n \n\n\n\n
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@article{luVisualTopicDiscovering2016,\n  title = {Visual Topic Discovering, Tracking and Summarization from Social Media Streams},\n  author = {Lu, Zhao and Lin, Yu-Ru and Huang, Xiaoxia and Xiong, Naixue and Fang, Zhijun},\n  year = {2016},\n  journal = {Multimedia Tools and Applications},\n  doi = {10.1007/s11042-016-3877-1},\n}\n\n
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\n \n\n \n \n Parra, D., Trattner, C., Gomez, D., Hurtado, M., Wen, X., & Lin, Y.\n\n\n \n \n \n \n Twitter in Academic Events: A Study of Temporal Usage, Communication, Sentimental and Topical Patterns in 16 Computer Science Conferences Computer Communications.\n \n \n \n\n\n \n\n\n\n Computer Communications, 73: 301–314. 2016.\n \n\n\n\n
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@article{parraTwitterAcademicEvents2016,\n  title = {Twitter in {{Academic Events}}: {{A Study}} of {{Temporal Usage}}, {{Communication}}, {{Sentimental}} and {{Topical Patterns}} in 16 {{Computer Science Conferences Computer Communications}}},\n  author = {Parra, Denis and Trattner, Christoph and Gomez, Diego and Hurtado, Matias and Wen, Xidao and Lin, Yu-Ru},\n  year = {2016},\n  journal = {Computer Communications},\n  volume = {73},\n  pages = {301--314},\n  doi = {10.1016/j.comcom.2015.07.001},\n}\n\n
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\n \n\n \n \n Cheng, R., Fan, Y., Jin, S., & Lin, Y.\n\n\n \n \n \n \n Visual Factoring of Historical Immigration Flows in the USA.\n \n \n \n\n\n \n\n\n\n In iConference 2016, 2016. \n \n\n\n\n
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@inproceedings{chengVisualFactoringHistorical2016,\n  title = {Visual {{Factoring}} of {{Historical Immigration Flows}} in the {{USA}}},\n  booktitle = {{{iConference}} 2016},\n  author = {Cheng, Ruoxuan and Fan, Yitian and Jin, Sanchuan and Lin, Yu-Ru},\n  year = {2016},\n}\n\n
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\n \n\n \n \n Chung, W., Wei, K., Lin, Y., & Wen, X.\n\n\n \n \n \n \n \n The Dynamics of Group Risk Perception in the US After Paris Attacks.\n \n \n \n \n\n\n \n\n\n\n In Proc. of the 8th International Conference on Social Informatics (SocInfo 2016), 2016. \n \n\n(Best Paper Award)\n\n
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@inproceedings{chungDynamicsGroupRisk2016,\n  title = {The {{Dynamics}} of {{Group Risk Perception}} in the {{US After Paris Attacks}}},\n  booktitle = {Proc. of the 8th {{International Conference}} on {{Social Informatics}} ({{SocInfo}} 2016)},\n  author = {Chung, Wen-Ting and Wei, Kai and Lin, Yu-Ru and Wen, Xidao},\n  year = {2016},\n  doi = {10.1007/978-3-319-47880-7_11},\n  url = {http://goo.gl/FIoSGP},\n  bibbase_note = {(Best Paper Award)},\n}\n\n
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\n \n\n \n \n Chung, W., & Lin, Y.\n\n\n \n \n \n \n Probing Construct Validity in Data-driven Disaster Analysis.\n \n \n \n\n\n \n\n\n\n In International Workshop on Collaborative Internet Computing for Disaster Management (CIC-DM 2016), 2016. \n \n\n\n\n
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@inproceedings{chungProbingConstructValidity2016,\n  title = {{Probing Construct Validity in Data-driven Disaster Analysis}},\n  booktitle = {{International Workshop on Collaborative Internet Computing for Disaster Management (CIC-DM 2016)}},\n  author = {Chung, Wen-Ting and Lin, Yu-Ru},\n  year = {2016},\n  doi = {10.1109/CIC.2016.076},\n}\n\n
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\n \n\n \n \n Farzan, R., Lu, D., & Lin, Y.\n\n\n \n \n \n \n What Happens Offline Stays Offline? Identifiers of the Sustainability of Hybrid Social Web Systems.\n \n \n \n\n\n \n\n\n\n In Proceedings of the 27th ACM Conference on Hypertext and Social Media (Hypertext 2016), of HT '16, 2016. ACM\n \n\n\n\n
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@inproceedings{farzanWhatHappensOffline2016,\n  title = {What {{Happens Offline Stays Offline}}? {{Identifiers}} of the {{Sustainability}} of {{Hybrid Social Web Systems}}},\n  booktitle = {Proceedings of the 27th {{ACM Conference}} on {{Hypertext}} and {{Social Media}} ({{Hypertext}} 2016)},\n  author = {Farzan, Rosta and Lu, Di and Lin, Yu-Ru},\n  year = {2016},\n  series = {{{HT}} '16},\n  publisher = {{ACM}},\n}\n\n
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\n \n\n \n \n He, X., & Lin, Y.\n\n\n \n \n \n \n Monitoring Collective Attention During Disasters.\n \n \n \n\n\n \n\n\n\n In International Workshop on Collaborative Internet Computing for Disaster Management (CIC-DM 2016), 2016. \n \n\n\n\n
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@inproceedings{heMonitoringCollectiveAttention2016,\n  title = {{Monitoring Collective Attention During Disasters}},\n  booktitle = {{International Workshop on Collaborative Internet Computing for Disaster Management (CIC-DM 2016)}},\n  author = {He, Xingsheng and Lin, Yu-Ru},\n  year = {2016},\n  doi = {10.1109/CIC.2016.068},\n}\n\n
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\n \n\n \n \n Jin, D., Wang, M., & Lin, Y.\n\n\n \n \n \n \n TELELINK: Link Prediction in Social Network Based on Multiplex Cohesive Structures.\n \n \n \n\n\n \n\n\n\n In 2016 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2016), 2016. \n \n\n\n\n
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@inproceedings{jinTELELINKLinkPrediction2016,\n  title = {{{TELELINK}}: {{Link Prediction}} in {{Social Network Based}} on {{Multiplex Cohesive Structures}}},\n  booktitle = {2016 {{International Conference}} on {{Social Computing}}, {{Behavioral-Cultural Modeling}} \\& {{Prediction}} and {{Behavior Representation}} in {{Modeling}} and {{Simulation}} ({{SBP-BRiMS}} 2016)},\n  author = {Jin, Di and Wang, Mengdi and Lin, Yu-Ru},\n  year = {2016},\n  doi = {10.1007/978-3-319-39931-7_17},\n}\n\n
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\n \n\n \n \n Li, L., Tong, H., Cao, N., Ehrlich, K., Lin, Y., & Buchler, N.\n\n\n \n \n \n \n TEAMOPT: Interactive Team Optimization in Big Networks.\n \n \n \n\n\n \n\n\n\n In Proc. of The 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), 2016. ACM\n \n\n\n\n
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@inproceedings{liTEAMOPTInteractiveTeam2016,\n  title = {{{TEAMOPT}}: {{Interactive Team Optimization}} in {{Big Networks}}},\n  booktitle = {Proc. of {{The}} 25th {{ACM International Conference}} on {{Information}} and {{Knowledge Management}} ({{CIKM}} 2016)},\n  author = {Li, Liangyue and Tong, Hanghang and Cao, Nan and Ehrlich, Kate and Lin, Yu-Ru and Buchler, Norbou},\n  year = {2016},\n  publisher = {{ACM}},\n}\n\n
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\n \n\n \n \n Liu, Z., Lin, Y., Wang, M., & Lu, Z.\n\n\n \n \n \n \n Discovering Opinion Changes in Online Reviews via Learning Fine-grained Sentiments.\n \n \n \n\n\n \n\n\n\n In Proceedings of 2016 IEEE International Conference on Collaboration and Internet Computing (IEEE CIC 2016), 2016. IEEE\n \n\n\n\n
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@inproceedings{liuDiscoveringOpinionChanges2016,\n  title = {Discovering {{Opinion Changes}} in {{Online Reviews}} via {{Learning Fine-grained Sentiments}}},\n  booktitle = {Proceedings of 2016 {{IEEE International Conference}} on {{Collaboration}} and {{Internet Computing}} ({{IEEE CIC}} 2016)},\n  author = {Liu, Zihang and Lin, Yu-Ru and Wang, Maoquan and Lu, Zhao},\n  year = {2016},\n  publisher = {{IEEE}},\n  doi = {10.1109/CIC.2016.015},\n}\n\n
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\n \n\n \n \n Lu, Z., Lin, Y., Zhang, Q., & Chen, M.\n\n\n \n \n \n \n Classifying Questions into Fine-grained Categories Using Topic Enriching.\n \n \n \n\n\n \n\n\n\n In Proceedings of 17th International Conference on Information Reuse and Integration (IEEE IRI 2016), 2016. \n \n\n\n\n
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@inproceedings{luClassifyingQuestionsFinegrained2016,\n  title = {Classifying {{Questions}} into {{Fine-grained}} Categories Using {{Topic Enriching}}},\n  booktitle = {Proceedings of 17th {{International Conference}} on {{Information Reuse}} and {{Integration}} ({{IEEE IRI}} 2016)},\n  author = {Lu, Zhao and Lin, Yu-Ru and Zhang, Qing and Chen, Mengwei},\n  year = {2016}\n}\n\n
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\n \n\n \n \n Sahebi, S., Lin, Y., & Brusilovsky, P.\n\n\n \n \n \n \n Tensor Factorization for Student Modeling and Performance Prediction in Unstructured Domain.\n \n \n \n\n\n \n\n\n\n In Machine Learning for Digital Education and Assessment Systems (MLDEAS), ICML 2016 Workshop, 2016. \n \n\n\n\n
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@inproceedings{sahebiTensorFactorizationStudent2016,\n  title = {{Tensor Factorization for Student Modeling and Performance Prediction in Unstructured Domain}},\n  booktitle = {{Machine Learning for Digital Education and Assessment Systems (MLDEAS), ICML 2016 Workshop}},\n  author = {Sahebi, Shaghayegh and Lin, Yu-Ru and Brusilovsky, Peter},\n  year = {2016},\n}\n\n
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\n \n\n \n \n Sahebi, S., Lin, Y., & Brusilovsky, P.\n\n\n \n \n \n \n \n Tensor Factorization for Student Modeling and Performance Prediction in Unstructured Domain.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of The 9th International Conference on Educational Data Mining (EDM 2016), 2016. \n \n\n\n\n
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@inproceedings{sahebiTensorFactorizationStudent2016a,\n  title = {Tensor {{Factorization}} for {{Student Modeling}} and {{Performance Prediction}} in {{Unstructured Domain}}},\n  booktitle = {Proceedings of {{The}} 9th {{International Conference}} on {{Educational Data Mining}} ({{EDM}} 2016)},\n  author = {Sahebi, Shaghayegh and Lin, Yu-Ru and Brusilovsky, Peter},\n  year = {2016},\n  url = {http://www.educationaldatamining.org/EDM2016/proceedings/paper_150.pdf},\n}\n\n
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\n \n\n \n \n Su, Y., Lan, Z., Lin, Y., Comfort, L. K., & Joshi, J.\n\n\n \n \n \n \n Tracking Disaster Response and Relief Following the 2015 Nepal Earthquake.\n \n \n \n\n\n \n\n\n\n In International Workshop on Collaborative Internet Computing for Disaster Management (CIC-DM 2016), 2016. \n \n\n\n\n
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@inproceedings{suTrackingDisasterResponse2016,\n  title = {{Tracking Disaster Response and Relief Following the 2015 Nepal Earthquake}},\n  booktitle = {{International Workshop on Collaborative Internet Computing for Disaster Management (CIC-DM 2016)}},\n  author = {Su, Yue and Lan, Ziyi and Lin, Yu-Ru and Comfort, Louise K. and Joshi, James},\n  year = {2016},\n  doi = {10.1109/CIC.2016.075},\n}\n\n
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\n \n\n \n \n Tsai, C., & Lin, Y.\n\n\n \n \n \n \n Tracing and Predicting Collaboration for Junior Scholars.\n \n \n \n\n\n \n\n\n\n In WWW 2016 BigScholar Workshop, WWW 2016 Companion Proceedings, 2016. \n \n\n\n\n
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@inproceedings{tsaiTracingPredictingCollaboration2016,\n  title = {{Tracing and Predicting Collaboration for Junior Scholars}},\n  booktitle = {{WWW 2016 BigScholar Workshop, WWW 2016 Companion Proceedings}},\n  author = {Tsai, Chun-Hua and Lin, Yu-Ru},\n  year = {2016},\n}\n\n
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\n \n\n \n \n Wang, M., & Lin, Y.\n\n\n \n \n \n \n Link Prediction via Multi-Hashing Framework.\n \n \n \n\n\n \n\n\n\n In 2016 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2016), 2016. \n \n\n\n\n
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@inproceedings{wangLinkPredictionMultiHashing2016,\n  title = {Link {{Prediction}} via {{Multi-Hashing Framework}}},\n  booktitle = {2016 {{International Conference}} on {{Social Computing}}, {{Behavioral-Cultural Modeling}} \\& {{Prediction}} and {{Behavior Representation}} in {{Modeling}} and {{Simulation}} ({{SBP-BRiMS}} 2016)},\n  author = {Wang, Mengdi and Lin, Yu-Ru},\n  year = {2016},\n  doi = {10.1007/978-3-319-39931-7_16},\n}\n\n
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\n \n\n \n \n Wei, K., & Lin, Y.\n\n\n \n \n \n \n \n The Evolution of Latino Threat Narrative from 1997 to 2014.\n \n \n \n \n\n\n \n\n\n\n In iConference 2016, 2016. \n \n\n(Best Poster Award Finalist)\n\n
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@inproceedings{weiEvolutionLatinoThreat2016,\n  title = {The {{Evolution}} of {{Latino Threat Narrative}} from 1997 to 2014},\n  booktitle = {{{iConference}} 2016},\n  author = {Wei, Kai and Lin, Yu-Ru},\n  year = {2016},\n  url = {https://www.ideals.illinois.edu/bitstream/handle/2142/89416/Wei582.pdf},\n  bibbase_note = {(Best Poster Award Finalist)},\n}\n\n
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\n \n\n \n \n Wen, X., Lin, Y., & Pelechrinis, K.\n\n\n \n \n \n \n \n PairFac: Event Analytics through Discriminant Tensor Factorization.\n \n \n \n \n\n\n \n\n\n\n In Proc. of The 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), 2016. \n \n\n\n\n
\n\n\n\n \n \n \"PairFac:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wenPairFacEventAnalytics2016,\n  title = {{{PairFac}}: {{Event Analytics}} through {{Discriminant Tensor Factorization}}},\n  booktitle = {Proc. of {{The}} 25th {{ACM International Conference}} on {{Information}} and {{Knowledge Management}} ({{CIKM}} 2016)},\n  author = {Wen, Xidao and Lin, Yu-Ru and Pelechrinis, Konstantinos},\n  year = {2016},\n  doi = {10.1145/2983323.2983837},\n  url = {http://goo.gl/fg7qYI},\n}\n\n
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\n \n\n \n \n Wen, X., Lu, D., Lin, Y., & Lopez, C.\n\n\n \n \n \n \n The Roles of Information Seeking Dynamics in Sustaining the Community Participation.\n \n \n \n\n\n \n\n\n\n In Proceedings of 2016 IEEE International Conference on Collaboration and Internet Computing (IEEE CIC 2016), 2016. IEEE\n \n\n\n\n
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@inproceedings{wenRolesInformationSeeking2016,\n  title = {The {{Roles}} of {{Information Seeking Dynamics}} in {{Sustaining}} the {{Community Participation}}},\n  booktitle = {Proceedings of 2016 {{IEEE International Conference}} on {{Collaboration}} and {{Internet Computing}} ({{IEEE CIC}} 2016)},\n  author = {Wen, Xidao and Lu, Di and Lin, Yu-Ru and Lopez, Claudia},\n  year = {2016},\n  publisher = {{IEEE}},\n}\n\n
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\n \n\n \n \n Wen, X., & Lin, Y.\n\n\n \n \n \n \n \n Sensing Distress Following A Terrorist Event.\n \n \n \n \n\n\n \n\n\n\n In 2016 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2016), 2016. \n \n\n\n\n
\n\n\n\n \n \n \"SensingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wenSensingDistressFollowing2016,\n  title = {Sensing {{Distress Following A Terrorist Event}}},\n  booktitle = {2016 {{International Conference}} on {{Social Computing}}, {{Behavioral-Cultural Modeling}} \\& {{Prediction}} and {{Behavior Representation}} in {{Modeling}} and {{Simulation}} ({{SBP-BRiMS}} 2016)},\n  author = {Wen, Xidao and Lin, Yu-Ru},\n  year = {2016},\n  doi = {10.1007/978-3-319-39931-7_36},\n  url = {http://goo.gl/2230pP},\n}\n\n
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\n \n\n \n \n Zhang, K., Lin, Y., & Pelechrinis, K.\n\n\n \n \n \n \n \n EigenTransitions with Hypothesis Testing: The Anatomy of Urban Mobility.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the 10th International AAAI Conference on Weblogs and Social Media (ICWSM 2016), 2016. \n \n\n\n\n
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@inproceedings{zhangEigenTransitionsHypothesisTesting2016,\n  title = {{{EigenTransitions}} with {{Hypothesis Testing}}: {{The Anatomy}} of {{Urban Mobility}}},\n  booktitle = {Proceedings of the 10th {{International AAAI Conference}} on {{Weblogs}} and {{Social Media}} ({{ICWSM}} 2016)},\n  author = {Zhang, Ke and Lin, Yu-Ru and Pelechrinis, Konstantinos},\n  year = {2016},\n  url = {http://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/view/13042/12768},\n  urldate = {2014-06-23},\n}\n\n
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\n  \n 2015\n \n \n (15)\n \n \n
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\n \n\n \n \n Cao, N., Lin, Y., & Gotz, D.\n\n\n \n \n \n \n UnTangle Map: Visual Analysis of Probabilistic Multi-Label Data.\n \n \n \n\n\n \n\n\n\n IEEE Transactions on Visualization and Computer Graphics (TVCG), 22(2): 1149–1163. 2015.\n \n\n\n\n
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@article{caoUnTangleMapVisual2015,\n  title = {{{UnTangle Map}}: {{Visual Analysis}} of {{Probabilistic Multi-Label Data}}},\n  author = {Cao, Nan and Lin, Yu-Ru and Gotz, David},\n  year = {2015},\n  journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)},\n  volume = {22},\n  number = {2},\n  pages = {1149--1163},\n  doi = {10.1109/TVCG.2015.2424878},\n}\n\n
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\n \n\n \n \n Lin, Y., Margolin, D., & Lazer, D.\n\n\n \n \n \n \n \n Uncovering Social Semantics From Textual Traces: A Theory-Driven Approach and Evidence From Public Statements of U.S. Members of Congress.\n \n \n \n \n\n\n \n\n\n\n Journal of the Association for Information Science and Technology (JASIST), 67(9): 2072–2089. 2015.\n \n\n\n\n
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@article{linUncoveringSocialSemantics2015,\n  title = {Uncovering {{Social Semantics From Textual Traces}}: {{A Theory-Driven Approach}} and {{Evidence From Public Statements}} of {{U}}.{{S}}. {{Members}} of {{Congress}}},\n  author = {Lin, Yu-Ru and Margolin, Drew and Lazer, David},\n  year = {2015},\n  journal = {Journal of the Association for Information Science and Technology (JASIST)},\n  volume = {67},\n  number = {9},\n  pages = {2072--2089},\n  doi = {10.1002/asi.23540},\n  url = {http://onlinelibrary.wiley.com/enhanced/doi/10.1002/asi.23540/},\n}\n\n
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\n \n\n \n \n Margolin, D., Goodman, S., Keegan, B., Lin, Y., & Lazer, D.\n\n\n \n \n \n \n Wiki-Worthy: Collective Judgment of Candidate Notability.\n \n \n \n\n\n \n\n\n\n Information, Communication and Society, 19(8): 1029–1045. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{margolinWikiWorthyCollectiveJudgment2015,\n  title = {Wiki-{{Worthy}}: {{Collective Judgment}} of {{Candidate Notability}}},\n  author = {Margolin, Drew and Goodman, Sasha and Keegan, Brian and Lin, Yu-Ru and Lazer, David},\n  year = {2015},\n  journal = {Information, Communication and Society},\n  volume = {19},\n  number = {8},\n  pages = {1029--1045},\n  doi = {10.1080/1369118X.2015.1069871},\n}\n\n
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\n \n\n \n \n Toole, J. L., Lin, Y., Muehlegger, E., Shoag, D., Gonzalez, M. C., & Lazer, D.\n\n\n \n \n \n \n \n Tracking Employment Shocks Using Mobile Phone Data.\n \n \n \n \n\n\n \n\n\n\n Journal of the Royal Society Interface (J. R. Soc. Interface), 12(107). 2015.\n \n\n\n\n
\n\n\n\n \n \n \"TrackingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{tooleTrackingEmploymentShocks2015,\n  title = {Tracking {{Employment Shocks Using Mobile Phone Data}}},\n  author = {Toole, Jameson L. and Lin, Yu-Ru and Muehlegger, Erich and Shoag, Daniel and Gonzalez, Marta C. and Lazer, David},\n  year = {2015},\n  journal = {Journal of the Royal Society Interface (J. R. Soc. Interface)},\n  volume = {12},\n  number = {107},\n  doi = {10.1098/rsif.2015.0185},\n  url = {http://arxiv.org/pdf/1505.06791v1.pdf},\n}\n\n
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\n \n\n \n \n Lin, Y.\n\n\n \n \n \n \n \n Event-Related Crowd Activities on Social Media.\n \n \n \n \n\n\n \n\n\n\n In Social Phenomena: From Data Analysis to Models. Springer, 2015.\n \n\n\n\n
\n\n\n\n \n \n \"Event-RelatedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@incollection{linEventrelatedCrowdActivities2015,\n  title = {Event-Related {{Crowd Activities}} on {{Social Media}}},\n  booktitle = {Social {{Phenomena}}: {{From Data Analysis}} to {{Models}}},\n  author = {Lin, Yu-Ru},\n  year = {2015},\n  publisher = {{Springer}},\n  url = {http://www.springer.com/978-3-319-14010-0},\n  isbn = {978-3-319-14011-7},\n}\n\n
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\n \n\n \n \n Cao, N., Lin, Y., Li, L., & Tong, H.\n\n\n \n \n \n \n \n G-Miner: Interactive Visual Group Mining on Multivariate Graphs.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2015), 2015. ACM\n \n\n\n\n
\n\n\n\n \n \n \"G-Miner:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{caoGMinerInteractiveVisual2015,\n  title = {G-{{Miner}}: {{Interactive Visual Group Mining}} on {{Multivariate Graphs}}},\n  shorttitle = {G-{{Miner}}},\n  booktitle = {Proceedings of the {{ACM SIGCHI Conference}} on {{Human Factors}} in {{Computing Systems}} ({{CHI}} 2015)},\n  author = {Cao, Nan and Lin, Yu-Ru and Li, Liangyue and Tong, Hanghang},\n  year = {2015},\n  publisher = {{ACM}},\n  url = {http://goo.gl/BtStZK},\n  urldate = {2015-03-12},\n}\n\n
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\n \n\n \n \n Cao, N., Shi, C., Lin, S., Lu, J., Lin, Y., & Lin, C.\n\n\n \n \n \n \n \n TargetVue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems.\n \n \n \n \n\n\n \n\n\n\n In IEEE Symposium on Visual Analytics Science and Technology (VAST 2015), 2015. \n \n\n\n\n
\n\n\n\n \n \n \"TargetVue:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{caoTargetVueVisualAnalysis2015,\n  title = {{{TargetVue}}: {{Visual Analysis}} of {{Anomalous User Behaviors}} in {{Online Communication Systems}}},\n  booktitle = {{{IEEE Symposium}} on {{Visual Analytics Science}} and {{Technology}} ({{VAST}} 2015)},\n  author = {Cao, Nan and Shi, Conglei and Lin, Sabrina and Lu, Jie and Lin, Yu-Ru and Lin, Ching-Yung},\n  year = {2015},\n  url = {http://bit.ly/targetvue},\n}\n\n
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\n \n\n \n \n Du, F., Cao, N., Zhao, J., & Lin, Y.\n\n\n \n \n \n \n \n Trajectory Bundling for Animated Transitions.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2015), 2015. ACM\n \n\n\n\n
\n\n\n\n \n \n \"TrajectoryPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{duTrajectoryBundlingAnimated2015,\n  title = {Trajectory {{Bundling}} for {{Animated Transitions}}},\n  shorttitle = {Traj},\n  booktitle = {Proceedings of the {{ACM SIGCHI Conference}} on {{Human Factors}} in {{Computing Systems}} ({{CHI}} 2015)},\n  author = {Du, Fan and Cao, Nan and Zhao, Jian and Lin, Yu-Ru},\n  year = {2015},\n  publisher = {{ACM}},\n  url = {http://goo.gl/EmBJ4S},\n  urldate = {2015-03-12},\n}\n\n
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\n \n\n \n \n Li, L., Tong, H., Cao, N., Ehrlich, K., Lin, Y., & Buchler, N.\n\n\n \n \n \n \n \n Replacing the Irreplaceable: Fast Algorithms for Team Member Recommendation.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the 24th International Conference on World Wide Web (WWW 2015), 2015. ACM\n \n\n\n\n
\n\n\n\n \n \n \"ReplacingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{liReplacingIrreplaceableFast2015,\n  title = {Replacing the {{Irreplaceable}}: {{Fast Algorithms}} for {{Team Member Recommendation}}},\n  booktitle = {Proceedings of the 24th {{International Conference}} on {{World Wide Web}} ({{WWW}} 2015)},\n  author = {Li, Liangyue and Tong, Hanghang and Cao, Nan and Ehrlich, Kate and Lin, Yu-Ru and Buchler, Norbou},\n  year = {2015},\n  publisher = {{ACM}},\n  url = {http://www.www2015.it/documents/proceedings/proceedings/p636.pdf},\n}\n\n
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\n \n\n \n \n Lopez, C., Lin, Y., & Farzan, R.\n\n\n \n \n \n \n \n What Makes Hyper-Local Online Discussion Forums Sustainable?.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of 48th Annual Hawaii International Conference on System Sciences (HICSS 2015), 2015. \n \n\n(Honorable Mention Award)\n\n
\n\n\n\n \n \n \"WhatPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{lopezWhatMakesHyperLocal2015,\n  title = {What {{Makes Hyper-Local Online Discussion Forums Sustainable}}?},\n  booktitle = {Proceedings of 48th {{Annual Hawaii International Conference}} on {{System Sciences}} ({{HICSS}} 2015)},\n  author = {Lopez, Claudia and Lin, Yu-Ru and Farzan, Rosta},\n  year = {2015},\n  url = {http://www.yurulin.com/download/pub/conference/lopez2015_hyperlocal_hicss.pdf},\n  bibbase_note = {(Honorable Mention Award)},\n}\n\n
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\n \n\n \n \n Lu, D., Lu, Y., Jeng, W., Farzan, R., & Lin, Y.\n\n\n \n \n \n \n \n Understanding Health Information Intent via Crowdsourcing: Challenges and Opportunities.\n \n \n \n \n\n\n \n\n\n\n In iConference 2015, 2015. \n \n\n(Best Poster Award Finalist)\n\n
\n\n\n\n \n \n \"UnderstandingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{luUnderstandingHealthInformation2015,\n  title = {Understanding {{Health Information Intent}} via {{Crowdsourcing}}: {{Challenges}} and {{Opportunities}}},\n  booktitle = {{{iConference}} 2015},\n  author = {Lu, Di and Lu, Yihang and Jeng, Wei and Farzan, Rosta and Lin, Yu-Ru},\n  year = {2015},\n  url = {http://hdl.handle.net/2142/73704},\n  bibbase_note = {(Best Poster Award Finalist)},\n}\n\n
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\n \n\n \n \n Margolin, D., Liao, D., & Lin, Y.\n\n\n \n \n \n \n \n Conversing in Reflective Glory: A Systematic Study Using National Football League Games.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the 9th International AAAI Conference on Weblogs and Social Media (ICWSM 2015), 2015. \n \n\n\n\n
\n\n\n\n \n \n \"ConversingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{margolinConversingReflectiveGlory2015,\n  title = {Conversing in {{Reflective Glory}}: {{A Systematic Study Using National Football League Games}}},\n  booktitle = {Proceedings of the 9th {{International AAAI Conference}} on {{Weblogs}} and {{Social Media}} ({{ICWSM}} 2015)},\n  author = {Margolin, Drew and Liao, David and Lin, Yu-Ru},\n  year = {2015},\n  url = {http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10567/10464},\n  urldate = {2014-06-23},\n}\n\n
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\n \n\n \n \n Tsai, C., & Lin, Y.\n\n\n \n \n \n \n \n The Evolution of Scientific Productivity of Junior Scholars.\n \n \n \n \n\n\n \n\n\n\n In iConference 2015, 2015. \n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{tsaiEvolutionScientificProductivity2015,\n  title = {The {{Evolution}} of {{Scientific Productivity}} of {{Junior Scholars}}},\n  booktitle = {{{iConference}} 2015},\n  author = {Tsai, Chun-Hua and Lin, Yu-Ru},\n  year = {2015},\n  url = {http://hdl.handle.net/2142/73728},\n}\n\n
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\n \n\n \n \n Wen, X., & Lin, Y.\n\n\n \n \n \n \n \n Information Seeking and Responding Networks in Physical Gatherings: A Case Study of Academic Conferences in Twitter.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the 2015 ACM on Conference on Online Social Networks (COSN 2015), pages 197–208, Stanford University, California, USA, 2015. ACM\n \n\n\n\n
\n\n\n\n \n \n \"InformationPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wenInformationSeekingResponding2015,\n  title = {Information {{Seeking}} and {{Responding Networks}} in {{Physical Gatherings}}: {{A Case Study}} of {{Academic Conferences}} in {{Twitter}}},\n  booktitle = {Proceedings of the 2015 {{ACM}} on {{Conference}} on {{Online Social Networks}} ({{COSN}} 2015)},\n  author = {Wen, Xidao and Lin, Yu-Ru},\n  year = {2015},\n  pages = {197--208},\n  publisher = {{ACM}},\n  address = {{Stanford University, California, USA}},\n  doi = {10.1145/2817946.2817960},\n  url = {http://goo.gl/9w182o},\n}\n\n
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\n \n\n \n \n Wen, X., & Lin, Y.\n\n\n \n \n \n \n \n Tweeting Questions in Academic Conferences: Seeking or Promoting Information?.\n \n \n \n \n\n\n \n\n\n\n In iConference 2015, 2015. \n \n\n\n\n
\n\n\n\n \n \n \"TweetingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wenTweetingQuestionsAcademic2015,\n  title = {Tweeting {{Questions}} in {{Academic Conferences}}: {{Seeking}} or {{Promoting Information}}?},\n  booktitle = {{{iConference}} 2015},\n  author = {Wen, Xidao and Lin, Yu-Ru},\n  year = {2015},\n  url = {http://hdl.handle.net/2142/73712},\n}\n\n
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\n  \n 2014\n \n \n (16)\n \n \n
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\n \n\n \n \n Cao, N., Lu, L., Lin, Y., Wang, F., & Wen, Z.\n\n\n \n \n \n \n \n SocialHelix: Visual Analysis of Sentiment Divergence in Social Media.\n \n \n \n \n\n\n \n\n\n\n Journal of Visualization, 18(2): 221–235. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"SocialHelix:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{caoSocialHelixVisualAnalysis2014,\n  title = {{{SocialHelix}}: {{Visual Analysis}} of {{Sentiment Divergence}} in {{Social Media}}},\n  shorttitle = {{{SocialHelix}}},\n  author = {Cao, Nan and Lu, Lu and Lin, Yu-Ru and Wang, Fei and Wen, Zhen},\n  year = {2014},\n  journal = {Journal of Visualization},\n  volume = {18},\n  number = {2},\n  pages = {221--235},\n  issn = {1343-8875, 1875-8975},\n  doi = {10.1007/s12650-014-0246-x},\n  url = {http://link.springer.com/article/10.1007/s12650-014-0246-x},\n  urldate = {2015-03-24},\n}\n\n
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\n \n\n \n \n Jin, L., Long, X., Zhang, K., Lin, Y., & Joshi, J.\n\n\n \n \n \n \n \n Characterizing Users' Check-in Activities Using Their Scores in a Location-based Social Network.\n \n \n \n \n\n\n \n\n\n\n Multimedia Systems, 22(1): 87—98. June 2014.\n \n\n\n\n
\n\n\n\n \n \n \"CharacterizingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{jinCharacterizingUsersCheckin2014,\n  title = {Characterizing {{Users}}' {{Check-in Activities Using Their Scores}} in a {{Location-based Social Network}}},\n  author = {Jin, Lei and Long, Xuelian and Zhang, Ke and Lin, Yu-Ru and Joshi, James},\n  year = {2014},\n  month = jun,\n  journal = {Multimedia Systems},\n  volume = {22},\n  number = {1},\n  pages = {87---98},\n  issn = {0942-4962, 1432-1882},\n  doi = {10.1007/s00530-014-0395-8},\n  url = {http://link.springer.com/article/10.1007/s00530-014-0395-8},\n  urldate = {2014-06-23},\n}\n\n
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\n \n\n \n \n Jin, L., Zhang, K., Lu, J., & Lin, Y.\n\n\n \n \n \n \n \n Towards Understanding the Gamification upon Users' Scores in a Location-based Social Network.\n \n \n \n \n\n\n \n\n\n\n Multimedia Tools and Applications,1–25. November 2014.\n \n\n\n\n
\n\n\n\n \n \n \"TowardsPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{jinUnderstandingGamificationUsers2014,\n  title = {Towards {{Understanding}} the {{Gamification}} upon {{Users}}' {{Scores}} in a {{Location-based Social Network}}},\n  author = {Jin, Lei and Zhang, Ke and Lu, Jianfeng and Lin, Yu-Ru},\n  year = {2014},\n  month = nov,\n  journal = {Multimedia Tools and Applications},\n  pages = {1--25},\n  issn = {1380-7501, 1573-7721},\n  doi = {10.1007/s11042-014-2317-3},\n  url = {http://link.springer.com/article/10.1007/s11042-014-2317-3},\n  urldate = {2015-03-24},\n}\n\n
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\n \n\n \n \n Lin, Y., & Margolin, D.\n\n\n \n \n \n \n \n The Ripple of Fear, Sympathy and Solidarity During the Boston Bombings.\n \n \n \n \n\n\n \n\n\n\n EPJ Data Science, 3(1): 31. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{linRippleFearSympathy2014,\n  title = {The {{Ripple}} of {{Fear}}, {{Sympathy}} and {{Solidarity During}} the {{Boston Bombings}}},\n  author = {Lin, Yu-Ru and Margolin, Drew},\n  year = {2014},\n  journal = {EPJ Data Science},\n  volume = {3},\n  number = {1},\n  pages = {31},\n  issn = {2193-1127},\n  doi = {10.1140/epjds/s13688-014-0031-z},\n  url = {http://epjds.epj.org/articles/epjdata/abs/2014/01/13688_2014_Article_31/13688_2014_Article_31.html},\n  urldate = {2015-01-05},\n}\n\n
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\n \n\n \n \n Lin, Y., Keegan, B., Margolin, D., & Lazer, D.\n\n\n \n \n \n \n \n Rising Tides or Rising Stars?: Dynamics of Shared Attention on Twitter during Media Events.\n \n \n \n \n\n\n \n\n\n\n PLoS ONE, 9(5): e94093. May 2014.\n \n\n\n\n
\n\n\n\n \n \n \"RisingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{linRisingTidesRising2014,\n  title = {Rising {{Tides}} or {{Rising Stars}}?: {{Dynamics}} of {{Shared Attention}} on {{Twitter}} during {{Media Events}}},\n  shorttitle = {Rising {{Tides}} or {{Rising Stars}}?},\n  author = {Lin, Yu-Ru and Keegan, Brian and Margolin, Drew and Lazer, David},\n  year = {2014},\n  month = may,\n  journal = {PLoS ONE},\n  volume = {9},\n  number = {5},\n  pages = {e94093},\n  doi = {10.1371/journal.pone.0094093},\n  url = {http://dx.doi.org/10.1371/journal.pone.0094093},\n  urldate = {2014-06-23},\n}\n\n
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\n \n\n \n \n Lin, Y., Margolin, D., & Lazer, D.\n\n\n \n \n \n \n \n Tracing Coordination and Cooperation Structures via Semantic Burst Detection.\n \n \n \n \n\n\n \n\n\n\n EAI Endorsed Transactions on Collaborative Computing, 1(2): e7. October 2014.\n \n\n\n\n
\n\n\n\n \n \n \"TracingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{linTracingCoordinationCooperation2014,\n  title = {Tracing {{Coordination}} and {{Cooperation Structures}} via {{Semantic Burst Detection}}},\n  author = {Lin, Yu-Ru and Margolin, Drew and Lazer, David},\n  year = {2014},\n  month = oct,\n  journal = {EAI Endorsed Transactions on Collaborative Computing},\n  volume = {1},\n  number = {2},\n  pages = {e7},\n  issn = {2312-8623},\n  doi = {10.4108/cc.1.2.e7},\n  url = {http://eudl.eu/doi/10.4108/cc.1.2.e7},\n  urldate = {2015-03-24},\n}\n\n
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\n \n\n \n \n Zhao, J., Cao, N., Wen, Z., Song, Y., Lin, Y., & Collins, C.\n\n\n \n \n \n \n \n #FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Visualization and Computer Graphics (TVCG), 20(12): 1773–1782. December 2014.\n \n\n\n\n
\n\n\n\n \n \n \"#FluxFlow:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{zhaoFluxFlowVisualAnalysis2014a,\n  title = {\\#{{FluxFlow}}: {{Visual Analysis}} of {{Anomalous Information Spreading}} on {{Social Media}}},\n  shorttitle = {\\#{{FluxFlow}}},\n  author = {Zhao, Jian and Cao, Nan and Wen, Zhen and Song, Yale and Lin, Yu-Ru and Collins, Christopher},\n  year = {2014},\n  month = dec,\n  journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)},\n  volume = {20},\n  number = {12},\n  pages = {1773--1782},\n  issn = {1077-2626},\n  doi = {10.1109/TVCG.2014.2346922},\n  url = {http://bit.ly/fluxflow-paper},\n}\n\n\n
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\n \n\n \n \n Wang, D., Lin, Y., & Bagrow, J. P.\n\n\n \n \n \n \n \n Learning Emergencies from Big Data.\n \n \n \n \n\n\n \n\n\n\n In Encyclopedia of Social Networks and Mining. Springer, 2014.\n \n\n\n\n
\n\n\n\n \n \n \"LearningPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@incollection{wangLearningEmergenciesBig2014,\n  title = {Learning {{Emergencies}} from {{Big Data}}},\n  booktitle = {Encyclopedia of {{Social Networks}} and {{Mining}}},\n  author = {Wang, Dashun and Lin, Yu-Ru and Bagrow, James P.},\n  year = {2014},\n  publisher = {{Springer}},\n  url = {http://www.springer.com/computer/communication+networks/book/978-1-4614-6169-2},\n  isbn = {978-1-4614-6169-2},\n}\n\n
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\n \n\n \n \n Guerra, J., Sahebi, S., Lin, Y., & Brusilovsky, P.\n\n\n \n \n \n \n \n The Problem Solving Genome: Analyzing Sequential Patterns of Student Work with Parameterized Exercises.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of The 7th International Conference on Educational Data Mining (EDM 2014), 2014. \n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{guerraProblemSolvingGenome2014,\n  title = {The {{Problem Solving Genome}}: {{Analyzing Sequential Patterns}} of {{Student Work}} with {{Parameterized Exercises}}},\n  booktitle = {Proceedings of {{The}} 7th {{International Conference}} on {{Educational Data Mining}} ({{EDM}} 2014)},\n  author = {Guerra, Julio and Sahebi, Shaghayegh and Lin, Yu-Ru and Brusilovsky, Peter},\n  year = {2014},\n  url = {http://goo.gl/VdFlHh},\n}\n\n
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\n \n\n \n \n Le, A., Lin, Y., & Pelechrinis, K.\n\n\n \n \n \n \n Information Network Mining: A Case For Emgergency Scenarios.\n \n \n \n\n\n \n\n\n\n In The 2014 KDD Workshop on Learning about Emergencies from Social Information (KDD-LESI 2014), 2014. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{leInformationNetworkMining2014,\n  title = {{Information Network Mining: A Case For Emgergency Scenarios}},\n  booktitle = {{The 2014 KDD Workshop on Learning about Emergencies from Social Information (KDD-LESI 2014)}},\n  author = {Le, Anh and Lin, Yu-Ru and Pelechrinis, Konstantinos},\n  year = {2014},\n  copyright = {10}\n}\n\n
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\n \n\n \n \n Lin, Y.\n\n\n \n \n \n \n \n Assessing Sentiment Segregation in Urban Communities.\n \n \n \n \n\n\n \n\n\n\n In International Conference on Social Computing (SocialCom 2014), 2014. ACE\n \n\n\n\n
\n\n\n\n \n \n \"AssessingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{linAssessingSentimentSegregation2014,\n  title = {Assessing {{Sentiment Segregation}} in {{Urban Communities}}},\n  booktitle = {International {{Conference}} on {{Social Computing}} ({{SocialCom}} 2014)},\n  author = {Lin, Yu-Ru},\n  year = {2014},\n  publisher = {{ACE}},\n  url = {http://goo.gl/5SPp5F},\n}\n\n
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\n \n\n \n \n Lin, Y.\n\n\n \n \n \n \n \n The Ripples of Fear, Comfort and Community Identity During the Boston Bombings.\n \n \n \n \n\n\n \n\n\n\n In iConference 2014, 2014. \n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{linRipplesFearComfort2014,\n  title = {The {{Ripples}} of {{Fear}}, {{Comfort}} and {{Community Identity During}} the {{Boston Bombings}}},\n  booktitle = {{{iConference}} 2014},\n  author = {Lin, Yu-Ru},\n  year = {2014},\n  doi = {10.9776/14331},\n  url = {https://www.ideals.illinois.edu/bitstream/handle/2142/47411/331_ready.pdf},\n}\n\n
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\n \n\n \n \n Lin, Y., Cao, N., Gotz, D., & Lu, L.\n\n\n \n \n \n \n \n UnTangle: Visual Mining for Data with Uncertain Multi-labels via Triangle Map.\n \n \n \n \n\n\n \n\n\n\n In 2014 IEEE International Conference on Data Mining (ICDM 2014), pages 340–349, December 2014. \n \n\n\n\n
\n\n\n\n \n \n \"UnTangle:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{linUnTangleVisualMining2014,\n  title = {{{UnTangle}}: {{Visual Mining}} for {{Data}} with {{Uncertain Multi-labels}} via {{Triangle Map}}},\n  shorttitle = {{{UnTangle}}},\n  booktitle = {2014 {{IEEE International Conference}} on {{Data Mining}} ({{ICDM}} 2014)},\n  author = {Lin, Yu-Ru and Cao, Nan and Gotz, David and Lu, Lu},\n  year = {2014},\n  month = dec,\n  pages = {340--349},\n  doi = {10.1109/ICDM.2014.24},\n  url = {http://goo.gl/x0p2JE},\n}\n\n
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\n \n\n \n \n Tsai, C., & Lin, Y.\n\n\n \n \n \n \n From Media Reporting to International Relations: A Case Study of Asia-Pacific Economic Cooperation (APEC).\n \n \n \n\n\n \n\n\n\n In Proceedings of Web Science 2014 (WebSci 2014), 2014. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{tsaiMediaReportingInternational2014,\n  title = {From {{Media Reporting}} to {{International Relations}}: {{A Case Study}} of {{Asia-Pacific Economic Cooperation}} ({{APEC}})},\n  booktitle = {Proceedings of {{Web Science}} 2014 ({{WebSci}} 2014)},\n  author = {Tsai, Chun-Hua and Lin, Yu-Ru},\n  year = {2014},\n}\n\n
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\n \n\n \n \n Wen, X., Lin, Y., Trattner, C., & Parra, D.\n\n\n \n \n \n \n \n Twitter in Academic Conferences: Usage, Networking and Participation over Time.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the 25th ACM Conference on Hypertext and Social Media (Hypertext 2014), of HT '14, pages 285–290, New York, NY, USA, 2014. ACM\n \n\n\n\n
\n\n\n\n \n \n \"TwitterPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wenTwitterAcademicConferences2014,\n  title = {Twitter in {{Academic Conferences}}: {{Usage}}, {{Networking}} and {{Participation}} over {{Time}}},\n  shorttitle = {Twitter in {{Academic Conferences}}},\n  booktitle = {Proceedings of the 25th {{ACM Conference}} on {{Hypertext}} and {{Social Media}} ({{Hypertext}} 2014)},\n  author = {Wen, Xidao and Lin, Yu-Ru and Trattner, Christoph and Parra, Denis},\n  year = {2014},\n  series = {{{HT}} '14},\n  pages = {285--290},\n  publisher = {{ACM}},\n  address = {{New York, NY, USA}},\n  doi = {10.1145/2631775.2631826},\n  url = {http://arxiv.org/abs/1403.7772},\n  urldate = {2015-03-25},\n  abstract = {Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, attendees and virtual attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we analyze the scholars' Twitter use in 16 Computer Science conferences over a timespan of five years. Our primary finding is that over the years there are increasing differences with respect to conversation use and information use in Twitter. We studied the interaction network between users to understand whether assumptions about the structure of the conversations hold over time and between different types of interactions, such as retweets, replies, and mentions. While `people come and people go,' we want to understand what keeps people staying engaged with the conference on Twitter. By casting the problem as a classification task, we find different factors that contribute to the continuing participation of users to the online Twitter conference activity. These results have implications for research communities to implement strategies for continuous and active participation among members.},\n  isbn = {978-1-4503-2954-5},\n}\n\n
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\n Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, attendees and virtual attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we analyze the scholars' Twitter use in 16 Computer Science conferences over a timespan of five years. Our primary finding is that over the years there are increasing differences with respect to conversation use and information use in Twitter. We studied the interaction network between users to understand whether assumptions about the structure of the conversations hold over time and between different types of interactions, such as retweets, replies, and mentions. While `people come and people go,' we want to understand what keeps people staying engaged with the conference on Twitter. By casting the problem as a classification task, we find different factors that contribute to the continuing participation of users to the online Twitter conference activity. These results have implications for research communities to implement strategies for continuous and active participation among members.\n
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\n \n\n \n \n Zhao, J., Cao, N., Wen, Z., Song, Y., Lin, Y., & Collins, C.\n\n\n \n \n \n \n \n #FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media.\n \n \n \n \n\n\n \n\n\n\n In IEEE Symposium on Visual Analytics Science and Technology (VAST 2014), 2014. \n \n\n(Honorable Mention Award in VAST 2014)\n\n
\n\n\n\n \n \n \"#FluxFlow:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{zhaoFluxFlowVisualAnalysis2014,\n  title = {\\#{{FluxFlow}}: {{Visual Analysis}} of {{Anomalous Information Spreading}} on {{Social Media}}},\n  booktitle = {{{IEEE Symposium}} on {{Visual Analytics Science}} and {{Technology}} ({{VAST}} 2014)},\n  author = {Zhao, Jian and Cao, Nan and Wen, Zhen and Song, Yale and Lin, Yu-Ru and Collins, Christopher},\n  year = {2014},\n  url = {http://bit.ly/fluxflow-paper},\n  bibbase_note = {(Honorable Mention Award in VAST 2014)},\n  keywords = {social media,visual analysis,visualization}\n}\n\n
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\n  \n 2013\n \n \n (1)\n \n \n
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\n \n\n \n \n Lin, Y., Lazer, D., & Cao, N.\n\n\n \n \n \n \n \n Watching How Ideas Spread Over Social Media.\n \n \n \n \n\n\n \n\n\n\n Leonardo, 46(3): 277–277. March 2013.\n \n\n\n\n
\n\n\n\n \n \n \"WatchingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{linWatchingHowIdeas2013,\n  title = {{Watching How Ideas Spread Over Social Media}},\n  author = {Lin, Yu-Ru and Lazer, David and Cao, Nan},\n  year = {2013},\n  month = mar,\n  journal = {Leonardo},\n  volume = {46},\n  number = {3},\n  pages = {277--277},\n  issn = {0024-094X},\n  doi = {10.1162/LEON_a_00573},\n  url = {http://dx.doi.org/10.1162/LEON_a_00573},\n  urldate = {2013-08-26},\n  abstract = {Social media, like Twitter, have been widely used for exchanging information, opinions and emotions about events happening across the world. The authors introduce a new visualization tool for tracing the process of information diffusion on social media in real time. The design highlights the social, spatiotemporal processes of diffusion based on a sunflower metaphor whose seeds are often dispersed far away. The design facilitates an understanding of when, where and how a piece of information is dispersed for large-scale events, including campaigns and earthquakes, as a tool witnessing today's information consumption and dispersion in the wild.}\n}\n\n
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\n Social media, like Twitter, have been widely used for exchanging information, opinions and emotions about events happening across the world. The authors introduce a new visualization tool for tracing the process of information diffusion on social media in real time. The design highlights the social, spatiotemporal processes of diffusion based on a sunflower metaphor whose seeds are often dispersed far away. The design facilitates an understanding of when, where and how a piece of information is dispersed for large-scale events, including campaigns and earthquakes, as a tool witnessing today's information consumption and dispersion in the wild.\n
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