People on Media: Jointly Identifying Credible News and Trustworthy Citizen Journalists in Online Communities
This addresses the issue of biased media and misinformation for online news consumers, though it is incremental as it builds on existing CRF models.
The paper tackles the problem of identifying credible news and trustworthy citizen journalists in online communities by developing a probabilistic graphical model that jointly analyzes interactions between users, news, and sources, achieving identification of credible articles, trustworthy sources, and expert users.
Media seems to have become more partisan, often providing a biased coverage of news catering to the interest of specific groups. It is therefore essential to identify credible information content that provides an objective narrative of an event. News communities such as digg, reddit, or newstrust offer recommendations, reviews, quality ratings, and further insights on journalistic works. However, there is a complex interaction between different factors in such online communities: fairness and style of reporting, language clarity and objectivity, topical perspectives (like political viewpoint), expertise and bias of community members, and more. This paper presents a model to systematically analyze the different interactions in a news community between users, news, and sources. We develop a probabilistic graphical model that leverages this joint interaction to identify 1) highly credible news articles, 2) trustworthy news sources, and 3) expert users who perform the role of "citizen journalists" in the community. Our method extends CRF models to incorporate real-valued ratings, as some communities have very fine-grained scales that cannot be easily discretized without losing information. To the best of our knowledge, this paper is the first full-fledged analysis of credibility, trust, and expertise in news communities.