Analyzing the Interplay between Diversity of News Recommendations and Misinformation Spread in Social Media. Pathak, R. & Spezzano, F. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, of UMAP Adjunct '24, pages 80–85, New York, NY, USA, June, 2024. Association for Computing Machinery. ![Analyzing the Interplay between Diversity of News Recommendations and Misinformation Spread in Social Media [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Recommender systems play a crucial role in social media platforms, especially in the context of news, by assisting users in discovering relevant news. However, these systems can inadvertently contribute to increased personalization, and the formation of filter bubbles and echo chambers, thereby aiding in the propagation of fake news or misinformation. This study specifically focuses on examining the tradeoffs between the diversity of news recommendations and the dissemination of misinformation on social media. We evaluated classical recommender algorithms on two Twitter (now X) datasets to assess the diversity of top-10 recommendation lists and simulated the propagation of recommended misinformation within the user network to analyze the impact of diversity on misinformation spread. The research findings indicate that an increase in news recommendation diversity indeed contributes to mitigating the propagation of misinformation. Additionally, collaborative and content-based recommender systems provide more diversity in comparison to popularity and network-based systems, resulting in less misinformation propagation. Our study underscores the crucial role of diversity recommendations in mitigating misinformation propagation, offering valuable insights for designing misinformation-aware recommender systems and diversity-based misinformation intervention.
@inproceedings{pathak_analyzing_2024,
address = {New York, NY, USA},
series = {{UMAP} {Adjunct} '24},
title = {Analyzing the {Interplay} between {Diversity} of {News} {Recommendations} and {Misinformation} {Spread} in {Social} {Media}},
isbn = {9798400704666},
url = {https://dl.acm.org/doi/10.1145/3631700.3664870},
doi = {10.1145/3631700.3664870},
abstract = {Recommender systems play a crucial role in social media platforms, especially in the context of news, by assisting users in discovering relevant news. However, these systems can inadvertently contribute to increased personalization, and the formation of filter bubbles and echo chambers, thereby aiding in the propagation of fake news or misinformation. This study specifically focuses on examining the tradeoffs between the diversity of news recommendations and the dissemination of misinformation on social media. We evaluated classical recommender algorithms on two Twitter (now X) datasets to assess the diversity of top-10 recommendation lists and simulated the propagation of recommended misinformation within the user network to analyze the impact of diversity on misinformation spread. The research findings indicate that an increase in news recommendation diversity indeed contributes to mitigating the propagation of misinformation. Additionally, collaborative and content-based recommender systems provide more diversity in comparison to popularity and network-based systems, resulting in less misinformation propagation. Our study underscores the crucial role of diversity recommendations in mitigating misinformation propagation, offering valuable insights for designing misinformation-aware recommender systems and diversity-based misinformation intervention.},
urldate = {2024-08-15},
booktitle = {Adjunct {Proceedings} of the 32nd {ACM} {Conference} on {User} {Modeling}, {Adaptation} and {Personalization}},
publisher = {Association for Computing Machinery},
author = {Pathak, Royal and Spezzano, Francesca},
month = jun,
year = {2024},
pages = {80--85},
}
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This study specifically focuses on examining the tradeoffs between the diversity of news recommendations and the dissemination of misinformation on social media. We evaluated classical recommender algorithms on two Twitter (now X) datasets to assess the diversity of top-10 recommendation lists and simulated the propagation of recommended misinformation within the user network to analyze the impact of diversity on misinformation spread. The research findings indicate that an increase in news recommendation diversity indeed contributes to mitigating the propagation of misinformation. Additionally, collaborative and content-based recommender systems provide more diversity in comparison to popularity and network-based systems, resulting in less misinformation propagation. 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