Article: Measuring echo chamber effects in youth online communities with temporal clustering Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.65 pp.93 - 121 Abstract: Echo chambers in youth online communities intensify opinion polarisation, yet existing methods rely on static snapshots and single-modal features, neglecting integration with social-psychological theories. This paper proposes the temporal clustering for echo chamber measurement framework, unifying dynamic graph neural networks with joint temporal clustering. Using Reddit data from 2018 to 2022, temporal user interaction networks are constructed, and theory-driven indicators - interaction homogeneity, topic convergence, and attitude polarisation - are extracted based on social identity and cognitive dissonance theories. A temporal graph attention network learns evolving node representations, followed by joint optimisation of temporal K-means and spectral clustering to identify echo chambers and quantify intensity trajectories. Experiments show the framework outperforms baselines, achieving an adjusted Rand index of 0.89 and an F1-score of 0.88. It captures echo chamber dynamics during major events, offering an interpretable tool for understanding online polarisation in youth communities. Inderscience Publishers - linking academia, business and industry through research

Title: Measuring echo chamber effects in youth online communities with temporal clustering

Authors: Xieer Jiang

Addresses: School of Journalism and Communication, Communication University of Zhejiang, Hangzhou, 310018, China

Abstract: Echo chambers in youth online communities intensify opinion polarisation, yet existing methods rely on static snapshots and single-modal features, neglecting integration with social-psychological theories. This paper proposes the temporal clustering for echo chamber measurement framework, unifying dynamic graph neural networks with joint temporal clustering. Using Reddit data from 2018 to 2022, temporal user interaction networks are constructed, and theory-driven indicators - interaction homogeneity, topic convergence, and attitude polarisation - are extracted based on social identity and cognitive dissonance theories. A temporal graph attention network learns evolving node representations, followed by joint optimisation of temporal K-means and spectral clustering to identify echo chambers and quantify intensity trajectories. Experiments show the framework outperforms baselines, achieving an adjusted Rand index of 0.89 and an F1-score of 0.88. It captures echo chamber dynamics during major events, offering an interpretable tool for understanding online polarisation in youth communities.

Keywords: echo chamber effect; temporal clustering; dynamic graph neural networks; youth online communities; social media analysis.

DOI: 10.1504/IJICT.2026.154182

International Journal of Information and Communication Technology, 2026 Vol.27 No.65, pp.93 - 121

Received: 09 Mar 2026
Accepted: 07 Apr 2026
Published online: 15 Jun 2026 *

Title: Measuring echo chamber effects in youth online communities with temporal clustering

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