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Ideological Fragmentation of the Social Media Ecosystem: From echo chambers to echo platforms

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arxiv 2411.16826 v2 pith:MQ7ITCWR submitted 2024-11-25 cs.CY cs.SIphysics.soc-ph

classification cs.CYcs.SIphysics.soc-ph
keywords platformsmediasocialechoecosystemideologicalusersanalyze
verification ladder T0 review T1 audit T2 compute T3 formal
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The entertainment-driven nature of social media encourages users to engage with like-minded individuals and consume content aligned with their beliefs, limiting exposure to diverse perspectives. Simultaneously, users migrate between platforms, either due to moderation policies like de-platforming or in search of environments better suited to their preferences. These dynamics drive the specialization of the social media ecosystem, shifting from internal echo chambers to "echo platforms"--entire platforms functioning as ideologically homogeneous niches. To systematically analyze this phenomenon in political discussions, we propose a quantitative approach based on three key dimensions: platform centrality, news consumption, and user base composition. We analyze 117 million posts related to the 2020 US Presidential elections from nine social media platforms--Facebook, Reddit, Twitter, YouTube, BitChute, Gab, Parler, Scored, and Voat. Our findings reveal significant differences among platforms in their centrality within the ecosystem, the reliability of circulated news, and the ideological diversity of their users, highlighting a clear divide between mainstream and alt-tech platforms. The latter occupy a peripheral role, feature a higher prevalence of unreliable content, and exhibit greater ideological uniformity. These results highlight the key dimensions shaping the fragmentation and polarization of the social media landscape.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Generative Exaggeration in LLM Social Agents: Consistency, Bias, and Toxicity

    cs.HC 2025-07 conditional novelty 5.0 of 10

    When LLMs are given more context about a real social media user, they become more ideologically consistent but also more extreme, toxic, and stereotyped than the user actually is.

  2. Involvement drives complexity of language in online debates

    cs.CL 2025-06 conditional novelty 4.0 of 10

    Influential Twitter users who are more partisan, negative, or offensive tend to use more lexically complex language, but the causal claim that involvement drives complexity is not supported.

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