Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions

By Chenhao Tan, Vlad Niculae, Cristian Danescu-Niculescu-Mizil, Lillian Lee. 2016

435 citations recorded by the source corpus.

Changing someone's opinion is arguably one of the most important challenges of social interaction. The underlying process proves difficult to study: it is hard to know how someone's opinions are formed and whether and how someone's views shift. Fortunately, ChangeMyView, an active community on Reddit, provides a platform where users present their own opinions and reasoning, invite others to contest them, and acknowledge when the ensuing discussions change their original views. In this work, we study these interactions to understand the mechanisms behind persuasion. We find that persuasive arguments are characterized by interesting patterns of interaction dynamics, such as participant entry-order and degree of back-and-forth exchange. Furthermore, by comparing similar counterarguments to the same opinion, we show that language factors play an essential role. In particular, the interplay between the language of the opinion holder and that of the counterargument provides highly predictive cues of persuasiveness. Finally, since even in this favorable setting people may not be persuaded, we investigate the problem of determining whether someone's opinion is susceptible to being changed at all. For this more difficult task, we show that stylistic choices in how the opinion is expressed carry predictive power.

Subfields: Artificial Intelligence, Communication, Sociology and Political Science, Natural Language Processing

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Citations in the corpus

Experience Grounds Language (2020, background): “Social interaction is a precious signal and some NLP researchers have began scratching the surface of it, especially for persuasion related tasks where interpersonal relationships are key (Wang et al., 2019c; Tan et al., 2016).”
Characterizing Online Discussion Using Coarse Discourse Sequences (2017, background): “Prior studies on question-answering (Harper et al. 2008), argumentation (Tan et al. 2016), echo chambers (Gilbert, Bergstrom, and Kara-halios 2009), and gratitude (Spiro, Matias, and Monroy-Hern´andez 2016) have used datasets signiﬁcantly smaller than our dataset or focused on only one or a few…”
Conversations Gone Awry: Detecting Early Signs of Conversational Failure (2018, methodology): “…of conversational behavior have also been used to predict conversation outcomes, such as betrayal in games (Niculae et al., 3 http://convokit.infosci.cornell.edu 2015), and success in team problem solving settings (Fu et al., 2017) or in persuading others (Tan et al., 2016; Zhang et al., 2016).”
Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features (2019, background): “We consider a number of hand-designed features related to the textual content of posts inspired by Tan et al. (2016).”
Argument Mining: A Survey (2020, background): “The most striking example of such are recent data sets gathered from the ChangeMyView (CMV) Reddit subcommunity25 (Tan et al. 2016; Hidey and McKeown 2018; Musi, Ghosh, and Muresan 2018).”
OpenAI o1 System Card (2024, neutral): “…human data from r/ChangeMyView , a popular subreddit with 4 million members that is an established and reputable resource for persuasion analysis[34]. r/ChangeMyView works as follows: • Users (denoted the “original poster” or OP) present their own opinions and supporting rationale (see example…”
A Just and Comprehensive Strategy for Using NLP to Address Online Abuse (2019, background): “ning positive behaviors such as how constructive conversations are (Kolhatkar and Taboada,2017;Napoles et al., 2017), whether dialog on contentious topics can exist without devolving into squabbling (Tan et al., 2016), or the level of support given between community members (Wang and Jurgens,2018). ”
DEBAGREEMENT: A comment-reply dataset for (dis)agreement detection in online debates (2021, background): “Specifically, one important research area is (dis)agreement detection, as it is fundamental for understanding societal polarisation and the spread of ideas online [31, 42, 34, 32].”