A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation

• URL: http://arxiv.org/abs/2112.05084v1
• Date: Thu, 9 Dec 2021 18:20:25 GMT
• Title: A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation
• Authors: Faisal Alatawi, Lu Cheng, Anique Tahir, Mansooreh Karami, Bohan Jiang, Tyler Black, Huan Liu
• Abstract summary: Echo chambers on social media are a significant problem that can elicit a number of negative consequences. We show the mechanisms, both algorithmic and psychological, that lead to the formation of echo chambers.
• Score: 13.299893581687702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Echo chambers on social media are a significant problem that can elicit a number of negative consequences, most recently affecting the response to COVID-19. Echo chambers promote conspiracy theories about the virus and are found to be linked to vaccine hesitancy, less compliance with mask mandates, and the practice of social distancing. Moreover, the problem of echo chambers is connected to other pertinent issues like political polarization and the spread of misinformation. An echo chamber is defined as a network of users in which users only interact with opinions that support their pre-existing beliefs and opinions, and they exclude and discredit other viewpoints. This survey aims to examine the echo chamber phenomenon on social media from a social computing perspective and provide a blueprint for possible solutions. We survey the related literature to understand the attributes of echo chambers and how they affect the individual and society at large. Additionally, we show the mechanisms, both algorithmic and psychological, that lead to the formation of echo chambers. These mechanisms could be manifested in two forms: (1) the bias of social media's recommender systems and (2) internal biases such as confirmation bias and homophily. While it is immensely challenging to mitigate internal biases, there has been great efforts seeking to mitigate the bias of recommender systems. These recommender systems take advantage of our own biases to personalize content recommendations to keep us engaged in order to watch more ads. Therefore, we further investigate different computational approaches for echo chamber detection and prevention, mainly based around recommender systems.

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