Towards Understanding Persuasion in Computational Argumentation
- URL: http://arxiv.org/abs/2110.01078v1
- Date: Sun, 3 Oct 2021 19:36:21 GMT
- Title: Towards Understanding Persuasion in Computational Argumentation
- Authors: Esin Durmus
- Abstract summary: Opinion formation and persuasion in argumentation are affected by three major factors: the argument itself, the source of the argument, and the properties of the audience.
This thesis makes several contributions to understand the relative effect of the source, audience, and language in computational persuasion.
- Score: 10.089382889894246
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Opinion formation and persuasion in argumentation are affected by three major
factors: the argument itself, the source of the argument, and the properties of
the audience. Understanding the role of each and the interplay between them is
crucial for obtaining insights regarding argument interpretation and
generation. It is particularly important for building effective argument
generation systems that can take both the discourse and the audience
characteristics into account. Having such personalized argument generation
systems would be helpful to expose individuals to different viewpoints and help
them make a more fair and informed decision on an issue. Even though studies in
Social Sciences and Psychology have shown that source and audience effects are
essential components of the persuasion process, most research in computational
persuasion has focused solely on understanding the characteristics of
persuasive language. In this thesis, we make several contributions to
understand the relative effect of the source, audience, and language in
computational persuasion. We first introduce a large-scale dataset with
extensive user information to study these factors' effects simultaneously.
Then, we propose models to understand the role of the audience's prior beliefs
on their perception of arguments. We also investigate the role of social
interactions and engagement in understanding users' success in online debating
over time. We find that the users' prior beliefs and social interactions play
an essential role in predicting their success in persuasion. Finally, we
explore the importance of incorporating contextual information to predict
argument impact and show improvements compared to encoding only the text of the
arguments.
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