Discovering User Interactions in Ideological Discussions

Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 671–681, Sofia, Bulgaria, August 4-9 2013. c⃝2013 Association for Computational Linguistics Discovering User Interactions in Ideological Discussions Arjun Mukherjee Bing Liu Department of Computer Science University of Illinois at Chicago arjun4787@gmail.com liub@cs.uic.edu Abstract Online discussion forums are a popular platform for people to voice their opinions on any subject matter and to discuss or debate any issue of interest. In forums where users discuss social, political, or religious issues, there are often heated debates among users or participants. Existing research has studied mining of user stances or camps on certain issues, opposing perspectives, and contention points. In this paper, we focus on identifying the nature of interactions among user pairs. The central questions are: How does each pair of users interact with each other? Does the pair of users mostly agree or disagree? What is the lexicon that people often use to express agreement and disagreement? We present a topic model based approach to answer these questions. Since agreement and disagreement expressions are usually multi- word phrases, we propose to employ a ranking method to identify highly relevant phrases prior to topic modeling. After modeling, we use the modeling results to classify the nature of interaction of each user pair. Our evaluation results using real-life discussion/debate posts demonstrate the effectiveness of the proposed techniques. 1 Introduction Online discussion/debate forums allow people with common interests to freely ask and answer questions, to express their views and opinions on any subject matter, and to discuss issues of common interest. A large part of such discussions is about social, political, and religious issues. On such issues, there are often heated discussions/debates, i.e., people agree or disagree and argue with one another. Such ideological discussions on a myriad of social and political issues have practical implications in the fields of communication and political science as they give social scientists an opportunity to study real-life discussions/debates of almost any issue and analyze participant behaviors in a large scale. In this paper, we present such an application, which aims to perform fine-grained analysis of user-interactions in online discussions. There have been some related works that focus on discovering the general topics and ideological perspectives in online discussions (Ahmed and Xing, 2010), placing users in support/oppose camps (Agarwal et al., 2003), and classifying user stances (Somasundaran and Wiebe, 2009). However, these works are at a rather coarser level and have not considered more fine-grained characteristics of debates/discussions where users interact with each other by quoting/replying each other to express agreement or disagreement and argue with one another. In this work, we want to mine the following information: 1. The nature of interaction of each pair of users or participants who have engaged in the discussion of certain issues, i.e., whether the two persons mostly agree or disagree with each other in their interactions. 2. What language expressions are often used to express agreement (e.g., “I agree” and “you’re right”) and disagreement (e.g., “I disagree” …

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