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Echo chambers on social media are a significant problem that can elicit anumber of negative consequences, most recently affecting the response toCOVID-19. Echo chambers promote conspiracy theories about the virus and arefound to be linked to vaccine hesitancy, less compliance with mask mandates,and the practice of social distancing. Moreover, the problem of echo chambersis connected to other pertinent issues like political polarization and thespread of misinformation. An echo chamber is defined as a network of users inwhich users only interact with opinions that support their pre-existing beliefsand opinions, and they exclude and discredit other viewpoints. This survey aimsto examine the echo chamber phenomenon on social media from a social computingperspective and provide a blueprint for possible solutions. We survey therelated literature to understand the attributes of echo chambers and how theyaffect the individual and society at large. Additionally, we show themechanisms, both algorithmic and psychological, that lead to the formation ofecho chambers. These mechanisms could be manifested in two forms: (1) the biasof social media's recommender systems and (2) internal biases such asconfirmation bias and homophily. While it is immensely challenging to mitigateinternal biases, there has been great efforts seeking to mitigate the bias ofrecommender systems. These recommender systems take advantage of our own biasesto personalize content recommendations to keep us engaged in order to watchmore ads. Therefore, we further investigate different computational approachesfor echo chamber detection and prevention, mainly based around recommendersystems.

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