Changing views: Persuasion modeling and argument extraction from online discussions

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Persuasion and argumentation are possibly among the most complex examples of the interplay between multiple human subjects. With the advent of the Internet, online forums provide wide platforms for people to share their opinions and reasonings around various diverse topics. In this work, we attempt to model persuasive interaction between users on Reddit, a popular online discussion forum. We propose a deep LSTM model to classify whether a conversation leads to a successful persuasion or not, and use this model to predict whether a certain chain of arguments can lead to persuasion. While learning persuasion dynamics, our model tends to identify argument facets implicitly, using an attention mechanism. We also propose a semi-supervised approach to extract argumentative components from discussion threads. Both these models provide useful insight into how people engage in argumentation on online discussion forums.

论文关键词:Persuasion modeling,Argument mining,Social media,Deep LSTM,Attention mechanism,Dynamic time warping distance

论文评审过程:Received 8 February 2019, Revised 12 July 2019, Accepted 12 July 2019, Available online 24 July 2019, Version of Record 13 January 2020.

论文官网地址:https://doi.org/10.1016/j.ipm.2019.102085