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arXiv Machine Learning By Abylaikhan Bexeit, Kushani Perera, Shanika Karunasekera, Jean Honorio

Revisiting the Objective of Echo Chamber Detection

Read the original on arXiv Machine Learning →

The paper introduces a formal objective for detecting echo chambers in social networks, distinguishing it from community detection, graph cut, and clique problems. It employs Fourier transform theory of set functions to define the objective and proposes a scalable semidefinite relaxation solved with interior point methods and sparse linear algebra. Experiments on synthetic and real datasets show the algorithm outperforms existing methods in recovering ground‑truth echo chambers and producing better network properties, including higher agreement with suspended users.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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