Analyzing the Impact of Filter Bubbles on Social Network Polarization

Chitra, Uthsav; Musco, Christopher

doi:10.1145/3336191.3371825

Cited by 85 publications

(51 citation statements)

References 40 publications

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“…Another opportunity of research comes with the association of social media with big data, data mining, and surveillance [22] that it can be used to better detect patterns of future outbreaks or consecutive (second) waves of a pandemic. AI can be added to this promising partnership as a powerful tool that can help develop, for instance, data-driven algorithms (using text mining or topic modeling) and insight-led methods to acquire patient and consumer's experiences of health and illness, for example, to discover and manage ''filter bubbles'' or ''community clusters'' that reinforce confirmation bias [23,24]. AI can assist with this and other future developments as a robust computational research tool.…”

Section: Big Data and Data Miningmentioning

confidence: 99%

Social media can have an impact on how we manage and investigate the COVID-19 pandemic

Cuello-Garcia

Pérez‐Gaxiola

Amelsvoort

2020

Journal of Clinical Epidemiology

167

117

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Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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Section: Big Data and Data Miningmentioning

confidence: 99%

Social media can have an impact on how we manage and investigate the COVID-19 pandemic

Cuello-Garcia

Pérez‐Gaxiola

Amelsvoort

2020

Journal of Clinical Epidemiology

167

117

View full text Add to dashboard Cite

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“…The distinction between these levels (which should be considered as a continuum, not a dichotomy) would help to reconcile some of the very conflicting claims and research findings about filter bubbles. Some studies at the technological level find evidence of extremely large filter bubbles by using mathematical models or simulations (e.g., Chitra & Musco, 2020). Studies that also take humans and the societal level into account, on the other hand, find virtually no evidence of filter bubbles, but often the complete opposite (for reviews, see Zuiderveen Borgesius et al, 2016;Bruns, 2019a).…”

Section: Filter Bubbles Can Be Seen At Two Levels: Technological and Societalmentioning

confidence: 99%

A critical review of filter bubbles and a comparison with selective exposure

Dahlgren

2021

Nordicom Review

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The new high-choice media environment has raised concerns that users of social networking sites primarily select political information that supports their political opinions and avoid information that challenges them. This behaviour is reinforced by personalisation algorithms that create filter bubbles and both narrow the available content and exclude challenging information over time. These concerns have, however, been contested. This article challenges the underlying theoretical assumptions about filter bubbles, and compares filter bubbles to what we already know about selective exposure and human psychology. The article lists nine counterarguments to the filter bubble thesis. In short, I argue that the assumptions of filter bubbles contradict many of the previous findings of selective exposure research. More specifically, when discussing filter bubbles there is a risk of confusing two arguments: one strong – but also trivial – that is about technology (e.g., personalisation leads to different information), and one weak and speculative – but also the most interesting – that is about society (e.g., personalisation increases political polarisation in society).

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“…Much of the work focusing on the implications of these interactions between users' choices and algorithms in online media focuses on the extent to which these interactions create ideological "filter bubbles" around political issues, or the trend of people to tailor their news networks to low-quality, highly partisan news with those choices then getting reinforced by the sites' algorithms [Pariser, 2011;Flaxman, Goel and Rao, 2016]. There is evidence to suggest that while curation algorithms do not promote homogeneity as a rule, they can be designed in such a way that creates homogeneity in networks [Berman and Katona, 2020;Chitra and Musco, 2020;Li et al, 2019;Min et al, 2019].…”

Section: Incidental Exposure and Knowledge Gaps Onlinementioning

confidence: 99%

Learning without seeking?: Incidental exposure to science news on social media & knowledge of gene editing

Anderson¹,

Howell²,

Xenos³

et al. 2021

JCOM

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Little is known about how incidental exposure to news, interpersonal discussion, and the diversity of social networks interact in social media environments and for science-related issues. Using a U.S. nationally representative survey, we investigate how these features relate to factual knowledge of gene editing. Incidental exposure to science-related news interacts with interpersonal discussion and network heterogeneity and reveals that the relationship between incidental exposure to news and knowledge is strongest among those who discuss the least. Incidental exposure could alleviate knowledge gaps between the Facebook users who are the most and least involved in interpersonal discussions about science.

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Analyzing the Impact of Filter Bubbles on Social Network Polarization

Cited by 85 publications

References 40 publications

Social media can have an impact on how we manage and investigate the COVID-19 pandemic

Social media can have an impact on how we manage and investigate the COVID-19 pandemic

A critical review of filter bubbles and a comparison with selective exposure

Learning without seeking?: Incidental exposure to science news on social media & knowledge of gene editing

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