Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/136811
Title: Echo chambers on social media: a systematic review of the literature
Author: Terren, Ludovic
Borge Bravo, Rosa
Others: Universitat Oberta de Catalunya (UOC)
Keywords: echo chambers
filter bubbles
social media
selective exposure
algorithmic curation
systematic literature review
Issue Date: 15-Mar-2021
Publisher: Review of Communication Research
Citation: Terren, L., & Borge-Bravo, R. (2021). Echo Chambers on Social Media: A Systematic Review of the Literature. Review of Communication Research, 9, 99-118. Retrieved from https://www.rcommunicationr.org/index.php/rcr/article/view/94
Abstract: There have been growing concerns regarding the potential impact of social media on democracy and public debate. While some theorists have claimed that ICTs and social media would bring about a new independent public sphere and increase exposure to political divergence, others have warned that they would lead to polarization through the formation of echo chambers. The issue of social media echo chambers is both crucial and widely debated. This article attempts to provide a comprehensive account of the scientific literature on this issue, shedding light on the different approaches, their similarities, differences, benefits, and drawbacks, and offering a consolidated and critical perspective that can hopefully support future research in this area. Concretely, it presents the results of a systematic review of 55 studies investigating the existence of echo chambers on social media, providing a first classification of the literature and identifying patterns across the studies¿ foci, methods and findings. We found that conceptual and methodological choices influence the results of research on this issue. Most importantly, articles that found clear evidence of echo chambers on social media were all based on digital trace data. In contrast, those that found no evidence were all based on self-reported data. Future studies should take into account the possible biases of the different approaches and the significant potential of combining self-reported data with digital trace data.
Language: English
URI: http://hdl.handle.net/10609/136811
ISSN: 2255-4165MIAR
Appears in Collections:Articles

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