People on Media: Jointly Identifying Credible News and Trustworthy Citizen Journalists in Online Communities

TL;DR

This paper introduces a probabilistic graphical model to analyze online news communities, aiming to identify credible news, trustworthy sources, and expert citizen journalists by leveraging complex user-news-source interactions.

Contribution

It presents the first comprehensive model for analyzing credibility, trust, and expertise in online news communities, extending CRF models to handle real-valued ratings.

Findings

Successfully identifies credible news articles

Accurately detects trustworthy sources

Finds expert citizen journalists

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.