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More Accounts, Fewer Links: How Algorithmic Curation Impacts Media Exposure in Twitter Timelines

Authors:

Nicholas DiakopoulosAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 5, Issue CSCW1

Article No.: 78, Pages 1 - 28

https://doi.org/10.1145/3449152

Published: 22 April 2021 Publication History

Abstract

Algorithmic timeline curation is now an integral part of Twitter's platform, affecting information exposure for more than 150 million daily active users. Despite its large-scale and high-stakes impact, especially during a public health emergency such as the COVID-19 pandemic, the exact effects of Twitter's curation algorithm generally remain unknown. In this work, we present a sock-puppet audit that aims to characterize the effects of algorithmic curation on source diversity and topic diversity in Twitter timelines. We created eight sock puppet accounts to emulate representative real-world users, selected through a large-scale network analysis. Then, for one month during early 2020, we collected the puppets' timelines twice per day. Broadly, our results show that algorithmic curation increases source diversity in terms of both Twitter accounts and external domains, even though it drastically decreases the number of external links in the timeline. In terms of topic diversity, algorithmic curation had a mixed effect, slightly amplifying a cluster of politically-focused tweets while squelching clusters of tweets focused on COVID-19 fatalities and health information. Finally, we present some evidence that the timeline algorithm may exacerbate partisan differences in exposure to different sources and topics. The paper concludes by discussing broader implications in the context of algorithmic gatekeeping.

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Graphs for each individual puppet that correspond to the aggregate average graphs in the paper.

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Duskin K(2024)Stemming the Tide of Problematic Information in Online Environments: Assessing Interventions and Identifying Opportunities for InterruptionCompanion Publication of the 16th ACM Web Science Conference10.1145/3630744.3658615(37-41)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3630744.3658615
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Index Terms

More Accounts, Fewer Links: How Algorithmic Curation Impacts Media Exposure in Twitter Timelines
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social media
2. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Content ranking
      2. Personalization

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 5, Issue CSCW1

CSCW

April 2021

5016 pages

EISSN:2573-0142

DOI:10.1145/3460939

Editor:
Jeff Nichols
Apple Inc., United States

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Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 22 April 2021

Published in PACMHCI Volume 5, Issue CSCW1

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Rose KRohlinger D(2024)Political Influencers and Their Social Media Audiences during the 2021 Arizona AuditSocius: Sociological Research for a Dynamic World10.1177/2378023124125968010Online publication date: 17-Jun-2024
https://doi.org/10.1177/23780231241259680
Stray JHalevy AAssar PHadfield-Menell DBoutilier CAshar ABakalar CBeattie LEkstrand MLeibowicz CMoon Sehat CJohansen SKerlin LVickrey DSingh SVrijenhoek SZhang AAndrus MHelberger NProutskova PMitra TVasan N(2024)Building Human Values into Recommender Systems: An Interdisciplinary SynthesisACM Transactions on Recommender Systems10.1145/36322972:3(1-57)Online publication date: 5-Jun-2024
https://dl.acm.org/doi/10.1145/3632297
Duskin K(2024)Stemming the Tide of Problematic Information in Online Environments: Assessing Interventions and Identifying Opportunities for InterruptionCompanion Publication of the 16th ACM Web Science Conference10.1145/3630744.3658615(37-41)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3630744.3658615
Hilbert MThakur AFlores PZhang XBhan JBernhard PJi F(2024)8–10% of algorithmic recommendations are ‘bad’, but… an exploratory risk-utility meta-analysis and its regulatory implicationsInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2023.10274375:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.ijinfomgt.2023.102743
Schwabl PHaim MUnkel J(2024)Aligning agent-based testing (ABT) with the experimental research paradigm: a literature review and best practicesJournal of Computational Social Science10.1007/s42001-024-00283-6Online publication date: 16-May-2024
https://doi.org/10.1007/s42001-024-00283-6
Bouchaud P(2024)Skewed perspectives: examining the influence of engagement maximization on content diversity in social media feedsJournal of Computational Social Science10.1007/s42001-024-00255-w7:1(721-739)Online publication date: 20-Mar-2024
https://doi.org/10.1007/s42001-024-00255-w
Bouchaud P(2024)Algorithmic Amplification of Politics and Engagement Maximization on Social MediaComplex Networks & Their Applications XII10.1007/978-3-031-53503-1_11(131-142)Online publication date: 29-Feb-2024
https://doi.org/10.1007/978-3-031-53503-1_11
Freelon DPruden MMalmer DWu QXia YJohnson DChen ECrist A(2024) What's in your PIE ? Understanding the contents of personalized information environments with PIEGraph Journal of the Association for Information Science and Technology10.1002/asi.24869Online publication date: 12-Jan-2024
https://doi.org/10.1002/asi.24869
Kristensen JHenriksen FMayerhöffer E(2023)Did COVID-19 Blur Partisan Boundaries? A Comparison of Partisan Affinity and Source Heterophily in Online Alternative News-Sharing Networks Before and During the COVID-19 PandemicSocial Media + Society10.1177/205630512311929639:3Online publication date: 26-Aug-2023
https://doi.org/10.1177/20563051231192963
Bartley NBurghardt KLerman KRokne JWang D(2023)Evaluating Content Exposure Bias in Social NetworksProceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3625007.3627724(379-383)Online publication date: 6-Nov-2023
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