Transformer-Based Quantification of the Echo Chamber Effect in Online Communities

An Echo Chamber on social media refers to the environment where like-minded people hear the echo of each others' voices, opinions, or beliefs, which reinforce their own. Echo Chambers can turn social media platforms into collaborative venues that polarize and radicalize users rather than broadening their exposure to diverse information. Having a quantified metric for measuring the Echo Chamber effect can aid moderators and policymakers in tracking and mitigating online polarization and radicalization. Existing methods for Echo Chamber detection are either one-dimensional, only considering the network behavior of users while ignoring their semantic behavior, or require demanding supervised labeling, which is both expensive and less generalizable.

This paper proposes a new metric to quantify the Echo Chamber effect using Transformer models for context-sensitive processing of natural language (NLP). Our metric quantifies (1) the effect of an Echo Chamber through the inverse effect of user diversity, and

(2) polarization by means of user separability between two Echo Chambers in a topic. Leveraging this metric, we further propose an NLP-based embedding that represents the users' activity. Our model is simultaneously effective, computationally cheap, and unsupervised.

As our method is unsupervised, it makes existing collaborative moderation efforts to thwart Echo Chamber effects more efficient by addressing the problem of identifying narrow information bases for algorithmic biases and misinformation detection. We run our analysis on three recent highly controversial political topics and a non-controversial topic: Russo-Ukrainian War, Abortion, Gun-Control, and SXSW music festival. Our results offer data-driven findings such as a higher Echo Chamber effect among Republicans over Democrats and diverse explicit support for Ukraine, especially among Democrats. We also observe a direct relationship between the Echo Chamber effect and polarization while observing that the low Echo Chamber effect for the Russo-Ukraine war is accompanied by a low polarization; and vice versa for Gun-Control.