Recognizing Stances in Online Debates

By Swapna Somasundaran, Janyce Wiebe. Annual Meeting of the Association for Computational Linguistics, 2009

327 citations recorded by the source corpus.

This paper presents an unsupervised opinion analysis method for debate-side classification, i.e., recognizing which stance a person is taking in an online debate. In order to handle the complexities of this genre, we mine the web to learn associations that are indicative of opinion stances in debates. We combine this knowledge with discourse information, and formulate the debate side classification task as an Integer Linear Programming problem. Our results show that our method is substantially better than challenging baseline methods.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/d5e79bf1a68f3f938ea36a4745bad917c28dc050

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In Artificial Intelligence

Citations in the corpus

Argumentation Mining in User-Generated Web Discourse (2016, background): “ument (discussion post, article) is assumed to represent the writer’s standpoint to the discussed topic. Since the topic is stated as a controversial question, the author is either for or against it. Somasundaran and Wiebe (2009) built a computational model for recognizing stances in dual-topic deba”
Stance Classification using Dialogic Properties of Persuasion (2012, background): “…Balahur et al., 2009; Bur-foot et al., 2011); (2) company-internal discussion sites (Murakami and Raymond, 2010; Agrawal et al., 2003); and (3) online social and political public forums (Somasundaran and Wiebe, 2009; Somasun-daran and Wiebe, 2010; Wang and Ros´e, 2010; Bi-ran and Rambow, 2011).”
A Corpus for Research on Deliberation and Debate (2012, methodology): “…text classiﬁcation and topic modeling and applying it to both face-to-face and online debate and deliberation (Lin et al., 2006; Thomas et al., 2006; Somasundaran and Wiebe, 2009; Somasundaran and Wiebe, 2010; Bansal et al., 2008; Greene and Resnik, 2009; Anand et al., 2011; Walker et al., ;…”
Sentiment analysis algorithms and applications: A survey (2014, background): “Stance is deﬁned as an overall position held by a person towards an object, idea or position [84].”
Recognizing Stances in Ideological On-Line Debates (2010, methodology): “Following (Somasundaran and Wiebe, 2009), stance , as used in this work, refers to an overall position held by a person toward an object, idea or proposition.”
Parsing Argumentation Structures in Persuasive Essays (2016, background): “They annotate the boundaries of argument components and determine the stance attribute of claims.”
An Unsupervised Neural Attention Model for Aspect Extraction (2017, methodology): “Following this, a number of methods have been proposed based on frequent item mining and dependency information to extract product aspects (Zhuang et al., 2006; Somasundaran and Wiebe, 2009; Qiu et al., 2011).”
Stance Detection on Social Media: State of the Art and Trends (2020, background): “Consequently, the early work on stance detection emerged in analyzing political debates on online forums [81, 91, 119, 127].”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, unknown): “Early work in stance detection, focused on congressional debates (Thomas, Pang, & Lee, 2006) or debates in online forums (Somasundaran & Wiebe, 2009; Murakami & Raymond, 2010; Anand, Walker, Abbott, Tree, Bowmani, & Minor, 2011; Walker, Anand, Abbott, & Grant, 2012; Hasan & Ng, 2013; Sridhar,…”