# Polarization Detection on Social Networks: dual contrastive objectives for Self-supervision

arXiv: https://arxiv.org/abs/2409.07716 · HTML: https://mlsanity.com/paper/2409.07716/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2024.

## Built on by (most cited first, 2 of 2)

- [A Natural Language Agentic Approach to Study Affective Polarization](https://mlsanity.com/paper/2603.02711/) (2026)
- [Lingo_Research_Group at SemEval-2026 Task 9: Evaluating Prompt Variants for Polarization Detection](https://mlsanity.com/paper/2606.03334/) (2026)

## Abstract (arXiv)

Echo chambers and online discourses have become prevalent social phenomena where communities engage in dramatic intra-group confirmations and inter-group hostility. Polarization detection is a rising research topic for detecting and identifying such polarized groups. Previous works on polarization detection primarily focus on hand-crafted features derived from dataset-specific characteristics and prior knowledge, which fail to generalize to other datasets. This paper proposes a unified self-supervised polarization detection framework, outperforming previous methods in unsupervised and semi-supervised polarization detection tasks on various publicly available datasets. Our framework utilizes a dual contrastive objective (DocTra): (1) interaction-level: to contrast between node interactions to extract critical features on interaction patterns, and (2) feature-level: to contrast extracted polarized and invariant features to encourage feature decoupling. Our experiments extensively evaluate our methods again 7 baselines on 7 public datasets, demonstrating significant performance improvements.