What is it about?
This paper proposes a framework to detect echo chambers in social media, by analyzing the propagation of information through the network. In particular, the paper presents a probabilistic generative model and its associated learning algorithm. The model describes how the social network structure and the propagation of information evolve through latent communities with different attributes. Our algorithm identifies and characterizes such communities, uncovering their degree of echo-chamber behavior.
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Why is it important?
Social media are often blamed for limiting the information users consume because of the subtle effects of algorithms. The so-called echo-chamber phenomenon has been identified as a possible culprit for the growing polarization observed in the online debate, potentially influencing society at large. To help evaluate the impact of this phenomenon, this paper provides a computational tool for both platforms and governments to actively monitor this crucial societal issue.
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This page is a summary of: Cascade-based Echo Chamber Detection, October 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3511808.3557253.
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