Achievement

In U.S. politics, opinions on a variety of issues are substantially though imperfectly correlated with each other and with party affiliation and with an overall self-identification as liberal or conservative. Thus, classifying people, media outlets, and opinions expressed in individual articles as liberal or conservative conveys meaning to most people.

We applied three semi-supervised learning methods that propagate classifications of political news articles and users as conservative or liberal, based on the assumption that liberal users will vote for liberal articles more often, and similarly for conservative users and articles. In cross-validation, the best algorithm achieved 99.6% accuracy on held-out users and 96.3% accuracy on held-out articles. The automatic classifier will be useful in the development of news services that try to prevent political polarization by nudging people reading a mixture of both liberal and conservative items. (Zhou, Resnick and Mei 2011)