# Changing Views: Persuasion Modeling and Argument Extraction from Online Discussions

## TL;DR

This paper models persuasion in online discussions using deep learning, predicting successful persuasion and extracting argument components to understand how persuasion occurs in social media debates.

## Contribution

It introduces a deep LSTM model with attention for persuasion prediction and a semi-supervised method for argument extraction in online forums.

## Key findings

- The model effectively predicts persuasion success in Reddit discussions.
- Attention mechanisms reveal implicit argument facets.
- Semi-supervised approach extracts argumentative components with useful insights.

## Abstract

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.