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Cited by 123Misinformation and Its ImpactsComplex Network Analysis TechniquesSocial Media and Politics

Trend of Narratives in the Age of Misinformation

Open access
Bessi, Alessandro, Zollo, Fabiana, Del Vicario, Michela, Scala, Antonio, Caldarelli, Guido, Quattrociocchi, Walter·2015·PLOS ONE
DOI 10.1371/journal.pone.0134641PMC PMC4537127
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Abstract

Social media enabled a direct path from producer to consumer of contents changing the way users get informed, debate, and shape their worldviews. Such a disintermediation might weaken consensus on social relevant issues in favor of rumors, mistrust, or conspiracy thinking-e.g., chem-trails inducing global warming, the link between vaccines and autism, or the New World Order conspiracy. Previous studies pointed out that consumers of conspiracy-like content are likely to aggregate in homophile clusters-i.e., echo-chambers. Along this path we study, by means of a thorough quantitative analysis, how different topics are consumed inside the conspiracy echo-chamber in the Italian Facebook. Through a semi-automatic topic extraction strategy, we show that the most consumed contents semantically refer to four specific categories: environment, diet, health, and geopolitics. We find similar consumption patterns by comparing users activity (likes and comments) on posts belonging to these different semantic categories. Finally, we model users mobility across the distinct topics finding that the more a user is active, the more he is likely to span on all categories. Once inside a conspiracy narrative users tend to embrace the overall corpus.

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  • The Louvain algorithm, Leiden algorithm, random walk modelling, greedy algorithms, latent space models, flow stability or hierarchical clustering algorithms are used in most cases of the included studies to detect communities in the bipartite graph.

    “The Louvain algorithm, 10 Leiden algorithm 11 (e.g., [99]), random walk modelling (e.g., [108, 127]), greedy algorithms (e.g., [94]), latent space models 12 (e.g., [8,100]), flow stability (e.g., [146]) or hierarchical clustering algorithms (e.g., [90]) are used in most cases of the included studies to detect communities in the bipartite graph.13”

    From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [127]

    The Methods (Preliminaries and Definitions) describe a bipartite network of posts and terms, then derive a term co-occurrence network from it. The Results (Topics extraction and validation) say the authors applied Walktrap, Multilevel, and Fast greedy community-detection algorithms to that co-occurrence network—not directly to the bipartite graph. This paper does not establish which methods were used in most included studies or report using Leiden, latent-space models, or flow stability. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • User leaning was also estimated by activity in specific groups, by semantics in the posts, calculation of political slant of visited websites by the user, and shared news URLs with news outlets leaning score.

    “User leaning was also estimated by activity in specific groups (e.g., [59,127,143]), by semantics in the posts (e.g., [94,99,113]), calculation of political slant of visited websites by the user [124], shared news URLs with news outlets leaning score (e.g., [8,96,129]).”

    From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [127]

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Research works printed but not linked: 37

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Cited by

Sorted by citations

Authors
Bessi, A., Zollo, F., Del Vicario, M., Scala, A., Caldarelli, G., & Quattrociocchi, W.
Affiliations
IMT School for Advanced Studies Lucca (IT)
Corresponding author
Quattrociocchi, Walter
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2015-08-14
Volume
10
Issue
8
Pages
e0134641
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0134641
PMC
PMC4537127
OpenAlex
W2163796701
Cited by
123
References
3
Citations by year
2026: 5 · 2025: 12 · 2024: 5 · 2023: 15 · 2022: 14 · 2021: 17 · 2020: 15 · 2019: 15 · 2018: 9 · 2017: 5 · 2016: 9 · 2015: 2
FWCI
25.66
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)
  • IMT Institutional Repository (IMT School for Advanced Studies Lucca) (acceptedVersion)
  • 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, cc-by-nc-nd)
  • arXiv (Cornell University)
Funders
  • European Commission (317532, 610704)
  • Division of Chemistry
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
conspiracy narratives, echo chambers, misinformation, social media, Facebook, user mobility, disintermediation, environmental topics, geopolitics, user engagement
MeSH terms
Communication, Diet, Environment, Health, Humans, Deception, Politics, Information Dissemination, Narration, Social Networking, Social Media

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