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

TL;DR

This paper introduces a self-supervised framework for polarization detection on social networks that leverages dual contrastive objectives to improve generalization and performance across multiple datasets.

Contribution

It proposes a novel dual contrastive learning approach for polarization detection that outperforms existing methods in unsupervised and semi-supervised settings.

Findings

Significant performance improvements over 7 baseline methods.

Effective in both unsupervised and semi-supervised polarization detection.

Generalizes well across diverse datasets.

Abstract

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.