2019/09/03 by Bibek Paudel, Paudel, Bibek, Abraham Bernstein +1
Computer Science · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Computers and Society (cs.CY) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Opinion Dynamics and Social Influence #Social Media and Politics #cs.CY #cs.IR
paper · pdf · doi:10.48550/arxiv.1909.01495
European Symposium Series on Societal Challenges in Computational Social Science, Zurich, Switzerland, September 2nd-4th, 2019
arxiv created 2019/09/03 · openalex publication_date 2019/09/03 · arxiv updated 2019/09/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The suggestions generated by most existing recommender systems are known to suffer from a lack of diversity, and other issues like popularity bias. As a result, they have been observed to promote well-known "blockbuster" items, and to present users with "more of the same" choices that entrench their existing beliefs and biases. This limits users' exposure to diverse viewpoints and potentially increases political polarization. To promote the diversity of views, we developed a novel computational framework that can identify the political leanings of users and the news items they share on online social networks. Based on such information, our system can recommend news items that purposefully expose users to different viewpoints and increase the diversity of their information "diet." Our research on recommendation diversity and political polarization helps us to develop algorithms that measure each user's reaction %to diverse viewpoints and adjust the recommendation accordingly. The result is an approach that exposes users to a variety of political views and will, hopefully, broaden their acceptance (not necessarily the agreement) of various opinions.