Brexit Means Brexit: Selection Bias, Echo Chambers, and Entrenched Opinion on Reddit

• URL: http://arxiv.org/abs/2603.07509v1
• Date: Sun, 08 Mar 2026 07:27:52 GMT
• Title: Brexit Means Brexit: Selection Bias, Echo Chambers, and Entrenched Opinion on Reddit
• Abstract summary: Political polarisation on structured discussion platforms such as Reddit differs fundamentally from that on broadcast platforms such as Twitter/X.<n>We present an end-to-end framework for measuring and analysing polarisation dynamics, applied to the r/Brexit subreddit.<n>We introduce a continuous polarity metric that replaces discrete stance categories, revealing fine-grained opinion spectra across 27 politically-defined periods.
• Score: 1.568136373506664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Political polarisation on structured discussion platforms such as Reddit differs fundamentally from that on broadcast platforms such as Twitter/X, yet most prior work targets the latter. We present an end-to-end framework for measuring and analysing polarisation dynamics, applied to the r/Brexit subreddit (871K submissions, November 2015 -- February 2021). We construct r/Brexit, a crowd-annotated stance dataset of 5,895 labelled submissions (inter-annotator agreement = 0.804), and train a domain-adapted BERT classifier. We introduce a continuous polarity metric that replaces discrete stance categories, revealing fine-grained opinion spectra across 27 politically-defined periods. Our analysis yields three key findings: (a) future stance prediction is confounded by survivorship bias: persuadable users disengage, and those who remain are already entrenched; (b) echo chambers are quantifiably dominant, with nearly 40% of interactions between like-minded users; (c) user current polarity is the dominant predictor of future polarity, with echo-chamber immersion as the secondary predictive signal. These findings reveal that Reddit's partisan core is entrenched by self-selection, not softened by cross-cutting exposure.

Related papers

• TTPO: Test-Time Policy Optimization [53.81524570216593]
  Test-Time Policy Optimization (TTPO) is an asymmetric objective that distills agreeing rollouts via OPSD and penalizes disagreeing rollouts with Grouped RL.<n>We show that TTPO matches label-supervised OPSD on five competition-level benchmarks, raises Qwen3-1.7B from 38.0% to 45.2% in TTT, yields +25.2% to +36.4% without thinking, and shows strong cross-task generalization.
  arXiv  Detail & Related papers  (2026-08-27T17:58:10Z)
• Asymmetric Discourse Homogenization and Shared Language Technology: Evidence from Reddit [2.152401683177439]
  I document an ideologically asymmetric break in the pre-existing diversification trend of political discourse, emerging around late 2022.<n>Conservative users experienced an interruption of their prior diversification trajectory; progressive users showed no comparable change.<n>The asymmetry is consistent across estimation strategies (ITS, DiD, RDiT, propensity-score matching) and temporal aggregations.
  arXiv  Detail & Related papers  (2026-08-13T18:13:35Z)
• Elite Polarization in European Parliamentary Speeches: a Novel Measurement Approach Using Large Language Models [0.0]
  This project introduces a new measure of elite polarization via actor and subject detection using artificial intelligence.<n>I identify when politicians mention one another in parliamentary speeches, note who is speaking and who is being addressed, and assess the emotional temperature behind these evaluations.<n>This maps how elites evaluate their various out-parties, allowing us to create an index of mutual out-party hostility, that is, elite polarization.
  arXiv  Detail & Related papers  (2025-07-09T08:44:29Z)
• Whose Opinions Do Language Models Reflect? [88.35520051971538]
  We investigate the opinions reflected by language models (LMs) by leveraging high-quality public opinion polls and their associated human responses. We find substantial misalignment between the views reflected by current LMs and those of US demographic groups. Our analysis confirms prior observations about the left-leaning tendencies of some human feedback-tuned LMs.
  arXiv  Detail & Related papers  (2023-03-30T17:17:08Z)
• Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
  We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases. We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions. Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
  arXiv  Detail & Related papers  (2023-01-14T18:58:42Z)
• Design and analysis of tweet-based election models for the 2021 Mexican legislative election [55.41644538483948]
  We use a dataset of 15 million election-related tweets in the six months preceding election day. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods.
  arXiv  Detail & Related papers  (2023-01-02T12:40:05Z)
• Reaching the bubble may not be enough: news media role in online political polarization [58.720142291102135]
  A way of reducing polarization would be by distributing cross-partisan news among individuals with distinct political orientations. This study investigates whether this holds in the context of nationwide elections in Brazil and Canada.
  arXiv  Detail & Related papers  (2021-09-18T11:34:04Z)
• Mundus vult decipi, ergo decipiatur: Visual Communication of Uncertainty in Election Polls [56.8172499765118]
  We discuss potential sources of bias in nowcasting and forecasting. Concepts are presented to attenuate the issue of falsely perceived accuracy. One key idea is the use of Probabilities of Events instead of party shares.
  arXiv  Detail & Related papers  (2021-04-28T07:02:24Z)
• Exploring Polarization of Users Behavior on Twitter During the 2019 South American Protests [15.065938163384235]
  We explore polarization on Twitter in a different context, namely the protest that paralyzed several countries in the South American region in 2019. By leveraging users' endorsement of politicians' tweets and hashtag campaigns with defined stances towards the protest (for or against), we construct a weakly labeled stance dataset with millions of users. We find empirical evidence of the "filter bubble" phenomenon during the event, as we not only show that the user bases are homogeneous in terms of stance, but the probability that a user transitions from media of different clusters is low.
  arXiv  Detail & Related papers  (2021-04-05T07:13:18Z)
• Towards control of opinion diversity by introducing zealots into a polarised social group [7.9603223299524535]
  We explore a method to influence or even control the diversity of opinions within a polarised social group. We leverage the voter model in which users hold binary opinions and repeatedly update their beliefs based on others they connect with. We inject zealots into a polarised network in order to shift the average opinion towards any target value.
  arXiv  Detail & Related papers  (2020-06-12T15:27:30Z)
• Embeddings-Based Clustering for Target Specific Stances: The Case of a Polarized Turkey [6.130136112098865]
  We present an unsupervised method for target-specific stance detection in a polarized setting, specifically Turkish politics. We show the effectiveness of our method in properly clustering users of divergent groups across multiple targets. We perform our analysis on a large dataset of 108M Turkish election-related tweets along with the timeline tweets of 168k Turkish users.
  arXiv  Detail & Related papers  (2020-05-19T13:52:15Z)
• Electoral Forecasting Using a Novel Temporal Attenuation Model: Predicting the US Presidential Elections [91.3755431537592]
  We develop a novel macro-scale temporal attenuation (TA) model, which uses pre-election poll data to improve forecasting accuracy. Our hypothesis is that the timing of publicizing opinion polls plays a significant role in how opinion oscillates, especially right before elections. We present two different implementations of the TA model, which accumulate an average forecasting error of 2.8-3.28 points over the 48-year period.
  arXiv  Detail & Related papers  (2020-04-30T09:21:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.