Pole

Abstract

From the 2016 U.S. presidential election to the 2021 Capitol riots to the spread of misinformation related to COVID-19, many have blamed social media for today's deeply divided society. Recent advances in machine learning for signed networks hold the promise to guide small interventions with the goal of reducing polarization in social media. However, existing models are especially ineffective in predicting conflicts (or negative links) among users. This is due to a strong correlation between link signs and the network structure, where negative links between polarized communities are too sparse to be predicted even by state-of-the-art approaches. To address this problem, we first design a partition-agnostic polarization measure for signed graphs based on the signed random-walk and show that many real-world graphs are highly polarized. Then, we propose POLE (POLarized Embedding for signed networks), a signed embedding method for polarized graphs that captures both topological and signed similarities jointly via signed autocovariance. Through extensive experiments, we show that POLE significantly outperforms state-of-the-art methods in signed link prediction, particularly for negative links with gains of up to one order of magnitude. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Information systems → Social networks.

Network polarization, filter bubbles, and echo chambers: an annotated review of measures and reduction methods

“…They discussed their strengths and weaknesses on examples of signed networks. Huang et al (2022) focused on predicting conflicts as negative links between users. According to the authors, negative links between polarized communities are too sparse to be predicted by stateof-the-art approaches.…”

A general model for how attributes can reduce polarization in social groups

“…In those proposed agent-based models, opinions evolve either toward consensus or polarized states. Here, we are interested in identity polarization (Rawlings, 2022) defined as individuals having positive and negative interactions toward in-group and outgroup members, respectively (Xiao et al, 2020;Huang et al, 2022). Polarization may be caused by simply disdaining the other side and not just by having a disagreement about policies (Mason, 2018) thus we measure polarization using the relationships between people.…”