Polarization Detection on Social Networks: dual contrastive objectives for Self-supervision
<|reference_start|>Polarization Detection on Social Networks: dual contrastive objectives for Self-supervision: 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.<|reference_end|>
@article{cui2024polarization,
    title={Polarization Detection on Social Networks: dual contrastive objectives
  for Self-supervision},
    author={Hang Cui, Tarek Abdelzaher},
    journal={arXiv preprint arXiv:2409.07716},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07716},
    primaryClass={cs.SI}
}