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\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
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(Best Paper Award of The Year)\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 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
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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 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
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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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