Title: People on Media: Jointly Identifying Credible News and Trustworthy Citizen Journalists in Online Communities,
Abstract: 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.