This paper examines the factors that govern persuasion for a priori UNDECIDED versus DECIDED audience members in the context of on-line debates. We separately study two types of influences: linguistic factors — features of the language of the debate itself; and audience factors — features of an audience member encoding demographic information, prior beliefs, and debate platform behavior. In a study of users of a popular debate platform, we find first that different combinations of linguistic features are critical for predicting persuasion outcomes for UNDECIDED versus DECIDED members of the audience. We additionally find that audience factors have more influence on predicting the side (PRO/CON) that persuaded UNDECIDED users than for DECIDED users that flip their stance to the opposing side. Our results emphasize the importance of considering the undecided and decided audiences separately when studying linguistic factors of persuasion.
more »
« less
Modeling the Factors of User Success in Online Debate
Debate is a process that gives individuals the opportunity to express, and to be exposed to, diverging viewpoints on controversial issues; and the existence of online debating platforms makes it easier for individuals to participate in debates and obtain feedback on their debating skills. But understanding the factors that contribute to a user’s success in debate is complicated: while success depends, in part, on the characteristics of the language they employ, it is also important to account for the degree to which their beliefs and personal traits are compatible with that of the audience. Friendships and previous interactions among users on the platform may further influence success. In this work, we aim to better understand the mechanisms behind success in online debates. In particular, we study the relative effects of debaters’ language, their prior beliefs and personal traits, and their social interactions with other users. We find, perhaps surprisingly, that characteristics of users’ social interactions play the most important role in determining their success in debates although the best predictive performance is achieved by combining social interaction features with features
more »
« less
- PAR ID:
- 10113367
- Date Published:
- Journal Name:
- The World Wide Web Conference
- Page Range / eLocation ID:
- 2701 to 2707
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Existing argumentation datasets have succeeded in allowing researchers to develop computational methods for analyzing the content, structure and linguistic features of argumentative text. They have been much less successful in fostering studies of the effect of “user” traits — characteristics and beliefs of the participants — on the debate/argument outcome as this type of user information is generally not available. This paper presents a dataset of 78,376 debates generated over a 10-year period along with surprisingly comprehensive participant profiles. We also complete an example study using the dataset to analyze the effect of selected user traits on the debate outcome in comparison to the linguistic features typically employed in studies of this kind.more » « less
-
Statistical methods applied to social media posts shed light on the dynamics of online dialogue. For example, users' wording choices predict their persuasiveness and users adopt the language patterns of other dialogue participants. In this paper, we estimate the causal effect of reply tones in debates on linguistic and sentiment changes in subsequent responses. The challenge for this estimation is that a reply's tone and subsequent responses are confounded by the users' ideologies on the debate topic and their emotions. To overcome this challenge, we learn representations of ideology using generative models of text. We study debates from 4Forums.com and compare annotated tones of replying such as emotional versus factual, or reasonable versus attacking. We show that our latent confounder representation reduces bias in ATE estimation. Our results suggest that factual and asserting tones affect dialogue and provide a methodology for estimating causal effects from text.more » « less
-
Social media has been at the center of discussions about political polarization in the United States. However, scholars are actively debating both the scale of political polarization online, and how important online polarization is to the offline world. One question at the center of this debate is what interactions across parties look like online, and in particular 1) whether increasing the number of such interactions is likely to increase or reduce polarization, and 2) what technological affordances may make it more likely that these cross-party interactions benefit, rather than detract from, existing political challenges. The present work aims to provide insights into the latter; that is, we focus on providing a better understanding of how a set of 400,000 partisan users on a particular social media platform, Twitter, used the platform's affordances to interact within and across parties in a large dataset of tweets about COVID in 2021. Our findings suggest that Republican use of cross-party interaction were both more potent and potentially more strategic during COVID, that cross-party interaction was driven heavily by a small set of users and conversations, and that there exist non-obvious indirect pathways to cross-party exposure when different modes of interaction are chained together (especially retweets of quotes). These findings have implications beyond Twitter, we believe, in understanding how affordances of platforms can help to shape partisan exposure and interaction.more » « less
-
Although social support can be a vital component of gender and sexual identity formation, many LGBTQ+ individuals often lack offline social networks for such support. Traditional online technologies also reveal several challenges in providing LGBTQ+ individuals with effective social support. Therefore, social VR, as a unique online space for immersive and embodied experiences, is becoming popular within LGBTQ+ communities for supportive online interactions. Drawing on 29 LGBTQ+ social VR users’ experiences, we investigate the types of social support LGBTQ+ users have experienced through social VR and how they leverage unique social VR features to experience such support. We provide one of the first empirical evidence of how social VR innovates traditional online support mechanisms to empower LGBTQ+ individuals but leads to new safety and equality concerns. We also propose important principles for rethinking social VR design to provide all users, rather than just the privileged few, with supportive experiences.more » « less
An official website of the United States government

