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

arXiv: https://arxiv.org/abs/1907.06076 · HTML: https://mlsanity.com/paper/1907.06076/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2019.

## Built on by (most cited first, 11 of 11)

- [Hate Speech and Counter Speech Detection: Conversational Context Does Matter](https://mlsanity.com/paper/2206.06423/) (2022)
- [Must Read: A Comprehensive Survey of Computational Persuasion](https://mlsanity.com/paper/2505.07775/) (2025)
- [Reddit Entity Linking Dataset](https://mlsanity.com/paper/2101.01228/) (2021)
- [Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining?](https://mlsanity.com/paper/2203.12881/) (2022)
- [Multilingual Persuasion Detection: Video Games as an Invaluable Data Source for NLP](https://mlsanity.com/paper/2207.04453/) (2022)
- [Hatred Stems from Ignorance! Distillation of the Persuasion Modes in Countering Conversational Hate Speech](https://mlsanity.com/paper/2403.15449/) (2024)
- [A Hybrid Theory and Data-driven Approach to Persuasion Detection with Large Language Models](https://mlsanity.com/paper/2511.22109/) (2025)
- [Can AI-Generated Persuasion Be Detected? Persuaficial Benchmark and AI vs. Human Linguistic Differences](https://mlsanity.com/paper/2601.04925/) (2026)
- [Enhancing AI Research Paper Analysis: Methodology Component Extraction using Factored Transformer-based Sequence Modeling Approach](https://mlsanity.com/paper/2311.03401/) (2023)
- [The GDN-CC Dataset: Automatic Corpus Clarification for AI-enhanced Democratic Citizen Consultations](https://mlsanity.com/paper/2601.14944/) (2026)
- [A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing](https://mlsanity.com/paper/2606.05330/) (2026)

## Abstract (arXiv)

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.