Cross-Cutting Political Awareness through Diverse News Recommendations

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.

[1]  Abraham Bernstein,et al.  Fewer Flops at the Top: Accuracy, Diversity, and Regularization in Two-Class Collaborative Filtering , 2017, RecSys.

[2]  M. Prior,et al.  Post-Broadcast Democracy: How Media Choice Increases Inequality in Political Involvement and Polarizes Elections , 2003 .

[3]  Jane Mansbridge,et al.  A systemic approach to deliberative democracy , 2012 .

[4]  A. Gamble.  The Realignment of British Politics in the Wake of Brexit , 2019, Political quarterly (London. 1930. Print).

[5]  Jacob Ratkiewicz,et al.  Predicting the Political Alignment of Twitter Users , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[6]  Sylvain Senecal,et al.  Using Recommendation Agents to Cope with Information Overload , 2012, Int. J. Electron. Commer..

[7]  Pablo Barberá.  Birds of the Same Feather Tweet Together. Estimating Policy Positions Using Twitter Data. , 2013 .

[8]  Abraham Bernstein,et al.  Blockbusters and Wallflowers: Accurate, Diverse, and Scalable Recommendations with Random Walks , 2015, RecSys.

[9]  Diana C. Mutz.  Cross-cutting Social Networks: Testing Democratic Theory in Practice , 2002, American Political Science Review.

[10]  Pablo Barberá.  Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data , 2015, Political Analysis.

[11]  Taylor W. Brown,et al.  Exposure to opposing views on social media can increase political polarization , 2018, Proceedings of the National Academy of Sciences.

[12]  Abraham Bernstein,et al.  Updatable, Accurate, Diverse, and Scalable Recommendations for Interactive Applications , 2016, ACM Trans. Interact. Intell. Syst..