Recognizing Stances in Ideological On-Line Debates

By Swapna Somasundaran, Janyce Wiebe. HLT-NAACL 2010, 2010

357 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3caa9a9f1d868068419cd47f85064a4cc37ef971

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Citations in the corpus

Stance and Sentiment in Tweets (2016, methodology): “Existing datasets for stance detection were created from online debate forums like 4forums.com and createdebates.com [Somasundaran and Wiebe 2010; Walker et al. 2012b; Hasan and Ng 2013].”
Linguistic Models for Analyzing and Detecting Biased Language (2013, background): “against/negative (Anand et al., 2011; Conrad et al., 2012; Somasundaran and Wiebe, 2010), or into one of two opposing views (Yano et al.”
SemEval-2016 Task 6: Detecting Stance in Tweets (2016, background): “Past work on stance detection includes that by Somasundaran and Wiebe (2010), Anand et al. (2011), Faulkner (2014), Rajadesingan and Liu (2014), Djemili et al. (2014), Boltuzic and ˇSnajder (2014), Conrad et al. (2012), Sridhar et al. (2014), Rajadesingan and Liu (2014), and Sobhani et al. (2015).”
Democrats, republicans and starbucks afficionados: user classification in twitter (2011, background): “Another area of focus has been understanding how the political orientation of a user is reflected in their writings: for example, [23, 13, 22] investigate congressional transcripts as well as informal user posts while [19] focuses on microblogs (we discuss the latter in more detail below).”
Using Summarization to Discover Argument Facets in Online Idealogical Dialog (2017, background): “And, indeed, previous work (Abbott et al., 2011; Somasundaran and Wiebe, 2010) has examined the structure of these discussions – e.”
A Corpus for Research on Deliberation and Debate (2012, background): “…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., ; Forsyth and Martell, 2007; Awadallah…”
Exploiting Social Network Structure for Person-to-Person Sentiment Analysis (2014, background): “…al., 2003; Mal-ouf and Mullen, 2008; Yu et al., 2008), bias (Yano et al., 2010; Recasens et al., 2013), and stance on debate topics (Thomas et al., 2006; Somasundaran and Wiebe, 2010; Lin et al., 2006; Anand et al., 2011), but these aspects of belief and social identity are not our primary concern.”
Stance Classification using Dialogic Properties of Persuasion (2012, methodology): “These properties may function to engage the audience and persuade them to form a particular opinion, but they make computational analysis of such debates challenging, with the best performance to date averaging 64% over several topics (Somasundaran and Wiebe, 2010).”
Zero-Shot Stance Detection: A Dataset and Model Using Generalized Topic Representations (2020, methodology): “…models for stance detection trained an individual classiﬁer for each topic (Lin et al., 2006; Beigman Klebanov et al., 2010; Srid-har et al., 2015; Somasundaran and Wiebe, 2010; Hasan and Ng, 2013; Li et al., 2018; Hasan and Ng, 2014) or for a small number of topics common to both the training…”
Computationally Detecting and Quantifying the Degree of Bias in Sentence-Level Text of News Stories (2015, background): “, [3]–[5]) or as one of two opposing views (e.”
Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions (2016, background): “Studies have also worked on understanding persuasive essays [18, 41, 51] and opinion analysis in terms of agreement and ideology [53, 49, 22, 50, 48].”
Limits of Electoral Predictions Using Twitter (2011, background): “In this sense, it would be very interesting to understand the impact of different lexicons and, even more important, to go one step further by using machine learning, as in the work of (Asur and Huberman 2010); or looking for a deeper understanding of the dynamics of political conversation in social”
Connotation Frames: A Data-Driven Investigation (2015, background): “People commonly express their opinions through subtle and nuanced language (Thomas et al., 2006; Somasundaran and Wiebe, 2010).”