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Cited by 345Misinformation and Its ImpactsComplex Network Analysis TechniquesSocial Media and Politics

The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.

Debunking in a world of tribes

Open access
Zollo, Fabiana, Bessi, Alessandro, Del Vicario, Michela, Scala, Antonio, Caldarelli, Guido, Shekhtman, Louis, Havlin, Shlomo, Quattrociocchi, Walter·2017·PLOS ONE
DOI 10.1371/journal.pone.0181821PMC PMC5524392
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Abstract

Social media aggregate people around common interests eliciting collective framing of narratives and worldviews. However, in such a disintermediated environment misinformation is pervasive and attempts to debunk are often undertaken to contrast this trend. In this work, we examine the effectiveness of debunking on Facebook through a quantitative analysis of 54 million users over a time span of five years (Jan 2010, Dec 2014). In particular, we compare how users usually consuming proven (scientific) and unsubstantiated (conspiracy-like) information on Facebook US interact with specific debunking posts. Our findings confirm the existence of echo chambers where users interact primarily with either conspiracy-like or scientific pages. However, both groups interact similarly with the information within their echo chamber. Then, we measure how users from both echo chambers interacted with 50,220 debunking posts accounting for both users consumption patterns and the sentiment expressed in their comments. Sentiment analysis reveals a dominant negativity in the comments to debunking posts. Furthermore, such posts remain mainly confined to the scientific echo chamber. Only few conspiracy users engage with corrections and their liking and commenting rates on conspiracy posts increases after the interaction.

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Crossref · 10.1371/journal.pone.0181821

Cites

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Figures

Fig 1 Users polarization. Probability density functions (PDFs) of the polarization of all users computed both on likes (left) and comments (right).
Fig 1 Users polarization. Probability density functions (PDFs) of the polarization of all users computed both on likes (left) and comments (right).CC BY · PubMed Central
Fig 2 Posts’ attention patterns and persistence. Left panel: Complementary cumulative distribution functions (CCDFs) of the number of likes, comments, and shares received by posts belonging to conspiracy (top) and scientific (bottom) news. Right panel: Kaplan-Meier estimates of survival functions of posts belonging to conspiracy and scientific news. Error bars are on the order of the size of the symbols.
Fig 2 Posts’ attention patterns and persistence. Left panel: Complementary cumulative distribution functions (CCDFs) of the number of likes, comments, and shares received by posts belonging to conspiracy (top) and scientific (bottom) news. Right panel: Kaplan-Meier estimates of survival functions of posts belonging to conspiracy and scientific news. Error bars are on the order of the size of the symbols.CC BY · PubMed Central
Fig 3 Users’ attention patterns and persistence. Left panel: Complementary cumulative distribution functions (CCDFs) of the number of comments (top), and likes (bottom), per each user on the two categories. Right panel: Kaplan-Meier estimates of survival functions for users on conspiracy and scientific news. Error bars are on the order of the size of the symbols.
Fig 3 Users’ attention patterns and persistence. Left panel: Complementary cumulative distribution functions (CCDFs) of the number of comments (top), and likes (bottom), per each user on the two categories. Right panel: Kaplan-Meier estimates of survival functions for users on conspiracy and scientific news. Error bars are on the order of the size of the symbols.CC BY · PubMed Central
Fig 4 Users’ activity on debunking posts. Proportions of likes (left) and comments (right) left by users polarized towards science, users polarized towards conspiracy, and not polarized users.
Fig 4 Users’ activity on debunking posts. Proportions of likes (left) and comments (right) left by users polarized towards science, users polarized towards conspiracy, and not polarized users.CC BY · PubMed Central
Fig 5 Users’ sentiment on debunking posts. Sentiment of comments made by all users (left), users polarized towards science (center), and users polarized towards conspiracy (right) on debunking posts having at least a like, a comment, and a share.
Fig 5 Users’ sentiment on debunking posts. Sentiment of comments made by all users (left), users polarized towards science (center), and users polarized towards conspiracy (right) on debunking posts having at least a like, a comment, and a share.CC BY · PubMed Central
Fig 6 Interaction with debunking: Survival functions and attention patterns. Top panel: Kaplan-Meier estimates of survival functions of users who interacted (exposed) and did not (not exposed) with debunking. Users persistence is computed both on their likes (left) and comments (right). Bottom panel: Complementary cumulative distribution functions (CCDFs) of the number of likes (left) and comments (right), per each user exposed and not exposed to debunking.
Fig 6 Interaction with debunking: Survival functions and attention patterns. Top panel: Kaplan-Meier estimates of survival functions of users who interacted (exposed) and did not (not exposed) with debunking. Users persistence is computed both on their likes (left) and comments (right). Bottom panel: Complementary cumulative distribution functions (CCDFs) of the number of likes (left) and comments (right), per each user exposed and not exposed to debunking.CC BY · PubMed Central
Fig 7 Interaction with debunking: Comments and likes rate. Rate—i.e., average number, over time, of likes (left) (resp., comments (right)) on conspiracy posts of users who interacted with debunking posts.
Fig 7 Interaction with debunking: Comments and likes rate. Rate—i.e., average number, over time, of likes (left) (resp., comments (right)) on conspiracy posts of users who interacted with debunking posts.CC BY · PubMed Central
Fig 8 Cox-Hazard model. Kaplan-Meier estimates of survival functions of users who interacted (exposed, orange) and did not (not exposed, green) with debunking and fits of the Cox proportional hazard model. Persistence of users is computed both on likes (left) and comments (right).
Fig 8 Cox-Hazard model. Kaplan-Meier estimates of survival functions of users who interacted (exposed, orange) and did not (not exposed, green) with debunking and fits of the Cox proportional hazard model. Persistence of users is computed both on likes (left) and comments (right).CC BY · PubMed Central

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Research works printed but not linked: 1

  1. Resnick P, Carton S, Park S, Shen Y, Zeffer N. RumorLens: A System for Analyzing the Impact of Rumors and Corrections in Social Media. In: Proc. Computational Journalism Conference; 2014.

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Metadata

Authors
Zollo, F., Bessi, A., Del Vicario, M., Scala, A., Caldarelli, G., Shekhtman, L., Havlin, S., & Quattrociocchi, W.
Affiliations
  • IMT School for Advanced Studies Lucca (IT)
  • Istituto Universitario di Studi Superiori di Pavia (IT)
  • Bar-Ilan University (IL)
Corresponding author
Zollo, Fabiana
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2017-07-24
Volume
12
Issue
7
Pages
e0181821
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0181821
PMC
PMC5524392
OpenAlex
W2962740444
Cited by
345
References
51
Citations by year
2026: 19 · 2025: 39 · 2024: 32 · 2023: 34 · 2022: 39 · 2021: 53 · 2020: 55 · 2019: 30 · 2018: 21 · 2017: 16
FWCI
85.15
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
  • ARCA (Università Ca' Foscari Venezia) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Social Sciences › Communication › Social Media and Politics
Keywords
debunking, misinformation, conspiracy beliefs, echo chambers, Facebook, social media, user engagement, sentiment analysis, scientific information, disintermediation
MeSH terms
Attention, Communication, Humans, United States, Social Networking, Social Media

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