Cross-Cutting Political Awareness through Diverse News Recommendations
This addresses the issue of political polarization for users of online social networks by promoting diverse news exposure, though it is incremental as it builds on existing recommender system methods.
The authors tackled the problem of limited diversity and political polarization in news recommendations by developing a computational framework that identifies political leanings and recommends diverse viewpoints, resulting in an approach that broadens users' acceptance of various opinions.
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.