Social Media platforms are increasingly being used for political activities and communication, and research suggests that social media design and use is contributing to the polarization of the public sphere. This study draws on Habermas' ideals concerning deliberative democracy to explore if novel interface designs that diversify information sources through content recommendation, can decrease polarization. Through a designprobe interview approach and insights generated from 19 political and citizen experts in Finland and the United States, we found that our deliberative design can lead to depolarization, while creating additional complexity through which users question content and information. We discuss the need to move beyond naive content recommendation, and user interface level changes, in order to work towards a depolarized public sphere.CCS Concepts: • Human-centered computing → Collaborative and social computing; Empirical studies in collaborative and social computing; Interaction design process and methods; • Applied computing → Law, social and behavioral sciences;
Fake news can significantly misinform people who often rely on online sources and social media for their information. Current research on fake news detection has mostly focused on analyzing fake news content and how it propagates on a network of users. In this paper, we emphasize the detection of fake news by assessing its credibility. By analyzing public fake news data, we show that information on news sources (and authors) can be a strong indicator of credibility. Our findings suggest that an author's history of association with fake news, and the number of authors of a news article, can play a significant role in detecting fake news. Our approach can help improve traditional fake news detection methods, wherein content features are often used to detect fake news.
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