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research-article

Personality traits and echo chambers on facebook

Author:

Alessandro BessiAuthors Info & Claims

Computers in Human Behavior, Volume 65, Issue C

Pages 319 - 324

https://doi.org/10.1016/j.chb.2016.08.016

Published: 01 December 2016 Publication History

Abstract

In online social networks, users tend to select information that adhere to their system of beliefs and to form polarized groups of like minded people. Polarization as well as its effects on online social interactions have been extensively investigated. Still, the relation between group formation and personality traits remains unclear. A better understanding of the cognitive and psychological determinants of online social dynamics might help to design more efficient communication strategies and to challenge the digital misinformation threat. In this work, we focus on users commenting posts published by US Facebook pages supporting scientific and conspiracy-like narratives, and we classify the personality traits of those users according to their online behavior. We show that different and conflicting communities are populated by users showing similar psychological profiles, and that the dominant personality model is the same in both scientific and conspiracy echo chambers. Moreover, we observe that the permanence within echo chambers slightly shapes users' psychological profiles. Our results suggest that the presence of specific personality traits in individuals lead to their considerable involvement in supporting narratives inside virtual echo chambers. We investigate the relation between group formation and personality traits.Conflicting communities are populated by users showing similar psychological profiles.Specific personality traits might lead users to join polarized communities.

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Cited By

Barman DConlan O(2024)Evaluating Prebunking and Nudge Techniques in Tackling Misinformation: A Between-Subject Study on Social Media PlatformsProceedings of the 35th ACM Conference on Hypertext and Social Media10.1145/3648188.3675125(167-177)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1145/3648188.3675125
Pathak RSpezzano FBoratto LGena CMarras MGermanakos PPopescus E(2024)Analyzing the Interplay between Diversity of News Recommendations and Misinformation Spread in Social MediaAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664870(80-85)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664870
Lim YHee MYee XYau WSim XTay WNg WNg SLee RChua TNgo CKumar RLauw HKa-Wei Lee R(2024)AISG's Online Safety Prize Challenge: Detecting Harmful Social Bias in Multimodal MemesCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3665993(1884-1891)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3665993
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Published In

cover image Computers in Human Behavior

Computers in Human Behavior Volume 65, Issue C

December 2016

680 pages

ISSN:0747-5632

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Copyright © Elsevier Ltd.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 December 2016

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Cited By

Barman DConlan O(2024)Evaluating Prebunking and Nudge Techniques in Tackling Misinformation: A Between-Subject Study on Social Media PlatformsProceedings of the 35th ACM Conference on Hypertext and Social Media10.1145/3648188.3675125(167-177)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1145/3648188.3675125
Pathak RSpezzano FBoratto LGena CMarras MGermanakos PPopescus E(2024)Analyzing the Interplay between Diversity of News Recommendations and Misinformation Spread in Social MediaAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664870(80-85)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664870
Lim YHee MYee XYau WSim XTay WNg WNg SLee RChua TNgo CKumar RLauw HKa-Wei Lee R(2024)AISG's Online Safety Prize Challenge: Detecting Harmful Social Bias in Multimodal MemesCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3665993(1884-1891)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3665993
Gambini MTardelli STesconi M(2024)The anatomy of conspiracy theoristsComputer Communications10.1016/j.comcom.2024.01.027217:C(25-40)Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.comcom.2024.01.027
Modgil SSingh RGupta SDennehy D(2024)A Confirmation Bias View on Social Media Induced Polarisation During Covid-19Information Systems Frontiers10.1007/s10796-021-10222-926:2(417-441)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10796-021-10222-9
Sheng MZhang P(2024)“How I Form and Escape Information Cocoons”: An Interview Study of Users on Short Video AppsWisdom, Well-Being, Win-Win10.1007/978-3-031-57850-2_10(129-138)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1007/978-3-031-57850-2_10
Pathak RSpezzano FPera M(2023)Understanding the Contribution of Recommendation Algorithms on Misinformation Recommendation and Misinformation Dissemination on Social NetworksACM Transactions on the Web10.1145/361608817:4(1-26)Online publication date: 14-Aug-2023
https://dl.acm.org/doi/10.1145/3616088
Srba IMoro RTomlein MPecher BSimko JStefancova EKompan MHrckova APodrouzek JGavornik ABielikova M(2023)Auditing YouTube’s Recommendation Algorithm for Misinformation Filter BubblesACM Transactions on Recommender Systems10.1145/35683921:1(1-33)Online publication date: 27-Jan-2023
https://dl.acm.org/doi/10.1145/3568392
Xie LWang DMa F(2023)Analysis of individual characteristics influencing user polarization in COVID-19 vaccine hesitancy.Computers in Human Behavior10.1016/j.chb.2022.107649143:COnline publication date: 1-Jun-2023
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Olshannikova ESkenderi EOlsson TKoivunen SHuhtamäki J(2022)Utilizing Structural Network Positions to Diversify People Recommendations on TwitterAdvances in Human-Computer Interaction10.1155/2022/65843942022Online publication date: 1-Jan-2022
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