STEntConv: Predicting Disagreement with Stance Detection and a Signed Graph Convolutional Network

Isabelle Lorge, Li Zhang, Xiaowen Dong, Janet B. Pierrehumbert
3/23/2024
cs.CL

No official code found. Best implementation: isabellelorge/contradiction (3 stars, last commit 2024-03-26).

Abstract

The rise of social media platforms has led to an increase in polarised online discussions, especially on political and socio-cultural topics such as elections and climate change. We propose a simple and novel unsupervised method to predict whether the authors of two posts agree or disagree, leveraging user stances about named entities obtained from their posts. We present STEntConv, a model which builds a graph of users and named entities weighted by stance and trains a Signed Graph Convolutional Network (SGCN) to detect disagreement between comment and reply posts. We run experiments and ablation studies and show that including this information improves disagreement detection performance on a dataset of Reddit posts for a range of controversial subreddit topics, without the need for platform-specific features or user history.

View on arXivView PDF

Code Implementations(1)

isabellelorge/contradictionCommunity100%
30PythonSep 13, 20222 years ago

Similar Papers

Decoding Climate Disagreement: A Graph Neural Network-Based Approach to Understanding Social Media Dynamics

84% match2024

A Survey of Stance Detection on Social Media: New Directions and Perspectives

81% match2024

CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning

81% match2024

Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networks

80% match2024

Social media polarization during conflict: Insights from an ideological stance dataset on Israel-Palestine Reddit comments

80% match2025

Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context

79% match2023

Cite this paper

@article{lorge2024stentconv,
  title  = {STEntConv: Predicting Disagreement with Stance Detection and a Signed Graph Convolutional Network},
  author = {Isabelle Lorge and Li Zhang and Xiaowen Dong and Janet B. Pierrehumbert},
  year   = {2024},
  eprint = {2403.15885},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.15885v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2403.15885.svg)](https://codeofpaper.com/paper/2403.15885)

Discussion