Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity

Valentin Hofmann; Xiaowen Dong; Janet Pierrehumbert; Hinrich Schuetze

NAACL 2022findings13 citations

Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity

Valentin Hofmann, Xiaowen Dong, Janet Pierrehumbert, Hinrich Schuetze

Abstract

The increasing polarization of online political discourse calls for computational tools that automatically detect and monitor ideological divides in social media. We introduce a minimally supervised method that leverages the network structure of online discussion forums, specifically Reddit, to detect polarized concepts. We model polarization along the dimensions of salience and framing, drawing upon insights from moral psychology. Our architecture combines graph neural networks with structured sparsity learning and results in representations for concepts and subreddits that capture temporal ideological dynamics such as right-wing and left-wing radicalization.

BibTeX

@inproceedings{hofmann-etal-2022-modeling,
    title = "Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity",
    author = "Hofmann, Valentin  and
      Dong, Xiaowen  and
      Pierrehumbert, Janet  and
      Schuetze, Hinrich",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-naacl.41/",
    doi = "10.18653/v1/2022.findings-naacl.41",
    pages = "536--550"
}