Platform Sorting Drives Ideological Fragmentation in the Social Media Ecosystem

• URL: http://arxiv.org/abs/2606.10575v1
• Date: Tue, 09 Jun 2026 08:41:17 GMT
• Title: Platform Sorting Drives Ideological Fragmentation in the Social Media Ecosystem
• Abstract summary: Ideological asymmetries in online political communication are often studied as localized phenomena emerging within communities.<n>Here, we show that fragmentation operates at the level of entire platforms, consistent with a process of platform sorting.<n>We analyze political information dynamics across Bluesky, Facebook, Reddit, Truth Social, Twitter/X, and YouTube during the 2020 and 2024 US presidential elections.
• Score: 38.151143956051705
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ideological asymmetries in online political communication are often studied as localized phenomena emerging within communities. Here, we show that fragmentation instead operates at the level of entire platforms, consistent with a process of platform sorting in which users increasingly align with ideologically congruent environments. We analyze political information dynamics across Bluesky, Facebook, Reddit, Truth Social, Twitter/X, and YouTube during the 2020 and 2024 US presidential elections, combining measures of content sharing, engagement allocation, and user-level ideological orientation. Across platforms, ideological fragmentation emerges consistently and persists over time. Platforms exhibit distinct ideological profiles that persist across the two election cycles, ranging from strongly left-leaning to strongly right-leaning environments. Longitudinal analyses further reveal limited ideological variability among persistent user cohorts, indicating that apparent changes within single platforms reflect ecosystem-level sorting rather than convergence toward neutrality. Taken together, our results show that the dynamics of platform sorting is not a transient reaction to political events or moderation interventions, but a persistent structural feature of the social media ecosystem.

Related papers

• Automated Identification of Competing Narratives in Political Discourse on Social Media [0.0]
  This paper presents an unsupervised framework for identifying and characterizing competing narratives in political discourse on social media.<n>The framework is able to detect the key competing narratives, highlighting the divergent framings and conflicts surrounding trending political topics.<n>The findings contribute to the broader understanding of how narratives propagate within the digital public sphere.
  arXiv  Detail & Related papers  (2026-09-10T08:10:33Z)
• Against Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media [17.163713154720806]
  TSN4PI is a unified framework for tracking the evolution of political ideologies on social media.<n>The PIDN uses large language models with style transfer and unsupervised domain adaptation to enable robust ideology detection.<n>The PIPN employs temporal graph neural networks to predict future ideological shifts.
  arXiv  Detail & Related papers  (2026-08-18T16:33:32Z)
• Quantifying Political Partisanship for Cross-Platform Analyses [0.8594140167290097]
  We propose a text-based, platform-portable methodology for measuring political partisanship in social media posts.<n>A partisanship axis is constructed in the embedding space as the difference between centroids of oppositely labeled clusters.<n>We apply the method to a corpus of approximately 1.3 million posts collected from Bluesky and Truth Social during the six months preceding the 2024 U.S. presidential election.
  arXiv  Detail & Related papers  (2026-07-23T22:12:17Z)
• Reddit Deplatforming and Toxicity Dynamics on Generalist Voat Communities [73.88859384645264]
  Deplatforming, the permanent banning of entire communities, is a primary tool for content moderation on mainstream platforms.<n>We analyze four major Reddit ban waves (2015--2020) and their effects on generalist communities on Voat.
  arXiv  Detail & Related papers  (2025-12-26T19:13:45Z)
• Latent Topic Synthesis: Leveraging LLMs for Electoral Ad Analysis [51.95395936342771]
  We introduce an end-to-end framework for automatically generating an interpretable topic taxonomy from an unlabeled corpus.<n>We apply this framework to a large corpus of Meta political ads from the month ahead of the 2024 U.S. Presidential election.<n>Our approach uncovers latent discourse structures, synthesizes semantically rich topic labels, and annotates topics with moral framing dimensions.
  arXiv  Detail & Related papers  (2025-10-16T20:30:20Z)
• Bridging the Narrative Divide: Cross-Platform Discourse Networks in Fragmented Ecosystems [9.119607936530038]
  Political discourse increasingly fragmented across different social networks.<n>To understand how narratives traverse fragmented ecosystems, we offer a structural lens for anticipating how narratives traverse ecosystems.<n>These findings offer implications for crossplatform governance, content moderation, and policy interventions.
  arXiv  Detail & Related papers  (2025-05-22T16:53:52Z)
• Ideology and polarization set the agenda on social media [32.6608163255408]
  This study analyzes large-scale Twitter data from three global debates--Climate Change, COVID-19, and the Russo-Ukrainian War--to investigate the structural dynamics of engagement.<n>Our findings reveal that discussions are not primarily shaped by specific categories of actors, such as media or activists, but by shared ideological alignment.
  arXiv  Detail & Related papers  (2024-12-06T16:48:22Z)
• Ideological Fragmentation of the Social Media Ecosystem: From echo chambers to echo platforms [39.58317527488534]
  We analyze 117 million posts related to the 2020 US Presidential elections from nine social media platforms.<n>Our findings reveal significant differences among platforms in their centrality within the ecosystem.<n>These results highlight the key dimensions shaping the fragmentation and polarization of the social media landscape.
  arXiv  Detail & Related papers  (2024-11-25T18:45:03Z)
• Understanding Divergent Framing of the Supreme Court Controversies: Social Media vs. News Outlets [56.67097829383139]
  We focus on the nuanced distinctions in framing of social media and traditional media outlets concerning a series of U.S. Supreme Court rulings. We observe significant polarization in the news media's treatment of affirmative action and abortion rights, whereas the topic of student loans tends to exhibit a greater degree of consensus.
  arXiv  Detail & Related papers  (2023-09-18T06:40:21Z)
• The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning [50.24983453990065]
  We use a deep-learning approach to process a sample of 220 YouTube videos of political leaders from 15 different countries.<n>We observe statistically significant differences in the average score of negative emotions between groups of leaders with varying degrees of populist rhetoric.
  arXiv  Detail & Related papers  (2023-04-19T18:32:49Z)
• 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)
• Quantifying social organization and political polarization in online platforms [2.66512000865131]
  We develop a methodology to quantify the positioning of online communities along social dimensions. Applying our methodology to 5.1B Reddit comments made in 10K communities over 14 years, we measure how the macroscale community structure is organized. We find Reddit underwent a significant polarization event around the 2016 U.S. presidential election, and remained highly polarized for years afterward.
  arXiv  Detail & Related papers  (2020-10-01T17:59:40Z)

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.