Political Ideology Detection Using Recursive Neural Networks

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 1113–1122, Baltimore, Maryland, USA, June 23-25 2014. c⃝2014 Association for Computational Linguistics Political Ideology Detection Using Recursive Neural Networks Mohit Iyyer1, Peter Enns2, Jordan Boyd-Graber3,4, Philip Resnik2,4 1Computer Science, 2Linguistics, 3iSchool, and 4UMIACS University of Maryland {miyyer,peter,jbg}@umiacs.umd.edu, resnik@umd.edu Abstract An individual’s words often reveal their po- litical ideology. Existing automated tech- niques to identify ideology from text focus on bags of words or wordlists, ignoring syn- tax. Taking inspiration from recent work in sentiment analysis that successfully models the compositional aspect of language, we apply a recursive neural network (RNN) framework to the task of identifying the po- litical position evinced by a sentence. To show the importance of modeling subsen- tential elements, we crowdsource political annotations at a phrase and sentence level. Our model outperforms existing models on our newly annotated dataset and an existing dataset. 1 Introduction Many of the issues discussed by politicians and the media are so nuanced that even word choice entails choosing an ideological position. For ex- ample, what liberals call the “estate tax” conser- vatives call the “death tax”; there are no ideolog- ically neutral alternatives (Lakoff, 2002). While objectivity remains an important principle of jour- nalistic professionalism, scholars and watchdog groups claim that the media are biased (Groseclose and Milyo, 2005; Gentzkow and Shapiro, 2010; Niven, 2003), backing up their assertions by pub- lishing examples of obviously biased articles on their websites. Whether or not it reflects an under- lying lack of objectivity, quantitative changes in the popular framing of an issue over time—favoring one ideologically-based position over another—can have a substantial effect on the evolution of policy (Dardis et al., 2008). Manually identifying ideological bias in polit- ical text, especially in the age of big data, is an impractical and expensive process. Moreover, bias They dubbed it the “death tax ” and created a big lie about its adverse effects on small businesses Figure 1: An example of compositionality in ideo- logical bias detection (red → conservative, blue → liberal, gray → neutral) in which modifier phrases and punctuation cause polarity switches at higher levels of the parse tree. may be localized to a small portion of a document, undetectable by coarse-grained methods. In this pa- per, we examine the problem of detecting ideologi- cal bias on the sentence level. We say a sentence contains ideological bias if its author’s political position (here liberal or conservative, in the sense of U.S. politics) is evident from the text. Ideological bias is difficult to detect, even for humans—the task relies not only on political knowledge but also on the annotator’s ability to pick up on subtle elements of language use. For example, the sentence in Figure 1 includes phrases typically associated with conservatives, such as “small businesses” and “death tax”. When we take more of the structure into account, however, we find that scare quotes and a negative propositional attitude (a lie about X) yield an evident liberal bias. Existing …

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