A Corpus for Research on Deliberation and Debate

By Marilyn A. Walker, J. E. Fox Tree, Pranav Anand, Rob Abbott, Joseph King. International Conference on Language Resources and Evaluation, 2012

306 citations recorded by the source corpus.

Deliberative, argumentative discourse is an important component of opinion formation, belief revision, and knowledge discovery; it is a cornerstone of modern civil society. Argumentation is productively studied in branches ranging from theoretical artificial intelligence to political rhetoric, but empirical analysis has suffered from a lack of freely available, unscripted argumentative dialogs. This paper presents the Internet Argument Corpus (IAC), a set of 390,704 posts in 11,800 discussions extracted from the online debate site 4forums.com. A 2866 thread/130,206 post extract of the corpus has been manually sided for topic of discussion, and subsets of this topic-labeled extract have been annotated for several dialogic and argumentative markers: degrees of agreement with a previous post, cordiality, audience-direction, combativeness, assertiveness, emotionality of argumentation, and sarcasm. As an application of this resource, the paper closes with a discussion of the relationship between discourse marker pragmatics, agreement, emotionality, and sarcasm in the IAC corpus.

View Paper: https://www.semanticscholar.org/paper/6d4b244deee49cd1bf8d28e2df9a08afb8828fb2

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Citations in the corpus

Humans Require Context to Infer Ironic Intent (so Computers Probably do, too) (2014, background): “Walker et al. (2012) also recently introduced the Internet Argument Corpus (IAC), which includes a sarcasm label (among others).”
Argument Mining: A Survey (2020, background): “The IAC is further developed in the IAC version 2 (Abbott et al. 2016), a collection of corpora for research in political debate on Internet forums.”
Challenges in Sentiment Analysis (2017, background): “Most of the earlier work focused on two-sided debates, for example on congressional debates (Thomas et al., 2006) or debates in online forums (Somasundaran and Wiebe, 2009; Murakami and Raymond, 2010; Anand et al., 2011; Walker et al., 2012; Hasan and Ng, 2013; Sridhar, Getoor, and Walker, 2014).”
Harnessing Context Incongruity for Sarcasm Detection (2015, methodology): “According to Wilson (2006), sarcasm arises from situational disparity.”
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].”
Stance Detection on Social Media: State of the Art and Trends (2020, methodology): “ach of these approaches are discussed in more detail. 7.1 Supervised learning This is the basic and most common approach for most of the work on stance detection [Zhang et al. 2019; Lai et al. 2020b; Walker et al. 2012a; Krejzl and Steinberger 2016; Igarashi et al. 2016; Gottipati et al. 2013]. With”
Towards Explainable Text Classification by Jointly Learning Lexicon and Modifier Terms (2017, unknown): “The IAC dataset is a subset of the Internet Argument Corpus [Walker et al., 2012] containing forum comments crawled from 4FORUMS on different topics: e. g., politics, . . . and labeled on a scale from -5 to 5.”
Most “babies” are “little” and most “problems” are “huge”: Compositional Entailment in Adjective-Nouns (2016, background): “We look at four different corpora capturing four different genres: Annotated Gigaword (Napoles et al., 2012) ( News ), image captions (Young et al., 2014) ( Image Captions ), the Internet Argument Corpus (Walker et al., 2012) ( Forums ), and the prose ﬁction subset of GutenTag dataset (Brooke et al.”
Stance Classification using Dialogic Properties of Persuasion (2012, unknown): “Our corpus is publicly available, see (Walker et al., 2012).”
Zero-Shot Stance Detection: A Dataset and Model Using Generalized Topic Representations (2020, background): “A number of datasets exist using the topic-phrase deﬁnition with texts from online debate forums (Walker et al., 2012; Abbott et al., 2016; Hasan and Ng, 2014), information platforms (Lin et al., 2006; Murakami and Putra, 2010), student essays (Faulkner, 2014), news comments (Krejzl et al., 2017;…”
A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version (2015, methodology): “This is aided by a dataset collected by Walker et al. (2012a) that we refer to as the Character Style from Film Corpus.”
Deep Learning for User Comment Moderation (2017, methodology): “6K comments (1K threads) from the Internet Argument Corpus (Walker et al., 2012; Abbott et al., 2016).”
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm (2017, methodology): “For sarcasm detection we use the sarcasm dataset version 1 and 2 from the Internet Argument Corpus (Walker et al., 2012).”