CoChat
Sign Up
Cited by 576Misinformation and Its ImpactsSocial Media and PoliticsComplex Network Analysis Techniques

Echo Chambers: Emotional Contagion and Group Polarization on Facebook

Open access
Del Vicario, Michela, Vivaldo, Gianna, Bessi, Alessandro, Zollo, Fabiana, Scala, Antonio, Caldarelli, Guido, Quattrociocchi, Walter·2016·Scientific Reports
DOI 10.1038/srep37825PMC PMC5131349
Open this paper in CoChat — free accountOpen PDF

Abstract

Recent findings showed that users on Facebook tend to select information that adhere to their system of beliefs and to form polarized groups – i.e., echo chambers. Such a tendency dominates information cascades and might affect public debates on social relevant issues. In this work we explore the structural evolution of communities of interest by accounting for users emotions and engagement. Focusing on the Facebook pages reporting on scientific and conspiracy content, we characterize the evolution of the size of the two communities by fitting daily resolution data with three growth models – i.e. the Gompertz model, the Logistic model, and the Log-logistic model. Although all the models appropriately describe the data structure, the Logistic one shows the best fit. Then, we explore the interplay between emotional state and engagement of users in the group dynamics. Our findings show that communities’ emotional behavior is affected by the users’ involvement inside the echo chamber. Indeed, to an higher involvement corresponds a more negative approach. Moreover, we observe that, on average, more active users show a faster shift towards the negativity than less active ones.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/srep37825

Cites

26 of 26 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Social media have recently been described as intensifying the echo-chamber effects that media often have anyway.

    “Social media have recently been described as intensifying the echochamber effects that media often have anyway, e.g. [46, 47, 48].”

    From AI for the Common Good?! Pitfalls, challenges, and ethics pen-testing (2019) · cites this paper as [47]

    The Abstract says social media shifted information selection away from mediation by journalists and that this encourages like-minded groups to form echo chambers. The Conclusions also describe Facebook as particularly suited to echo chambers. The paper does not establish that media generally have these effects already or directly compare their intensity with social media. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Challenge avoidance and reinforcement seeking, paired with peer influence, promote the formation of tightly clustered subgroups within echo chambers.

    “Studies (e.g., [97,98]) found that these mechanisms, paired with peer influence, promote the formation of tightly clustered subgroups within echo chambers.”

    From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [98]

    The Abstract (page unavailable) links confirmation bias to selecting belief-consistent information and forming like-minded groups, and says social norms may affect whether users accept news. It also says dissenting information is mainly ignored. The paper does not establish that these mechanisms and peer influence produce tightly clustered subgroups within echo chambers; it describes the echo chambers themselves as polarized communities. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Selective exposure and confirmation bias may explain the emergence of echo chambers on social media.

    “Selective exposure (19) and confirmation bias (20) (i.e., the tendency to seek information adhering to preexisting opinions) may explain the emergence of echo chambers on social media (1, 17, 21, 22).”

    From The echo chamber effect on social media (2021) · cites this paper as [21]

  • Los usuarios más activos tienden a mostrar mayor negatividad emocional que los menos activos.

    “los usuarios más activos tienden a mostrar mayor negatividad emocional que los menos activos (Del Vicario et al., 2016)”

    From Emociones y polarización de las comunidades digitales en América Latina: elecciones presidenciales 2018-2019 (2023) · cites this paper as Del Vicario et al., 2016

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. Zollo F.. Debunking in a world of tribes, http://arxiv.org/abs/1510.04267 (2015). · arXiv
  2. Allen M. R. & Smith L. A. Monte Carlo SSA: detecting irregular oscillations in the presence of colored noise. Journal of Climate 9, 3373– 3404 (1996).

Cited by

Sorted by citations

Related

Authors
Del Vicario, M., Vivaldo, G., Bessi, A., Zollo, F., Scala, A., Caldarelli, G., & Quattrociocchi, W.
Affiliations
IMT School for Advanced Studies Lucca (IT)
Corresponding author
Quattrociocchi, Walter
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2016-12-01
Volume
6
Issue
1
Pages
37825-37825
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/srep37825
PMC
PMC5131349
OpenAlex
W2466507159
Cited by
576
References
26
Citations by year
2026: 50 · 2025: 61 · 2024: 67 · 2023: 72 · 2022: 90 · 2021: 87 · 2020: 74 · 2019: 46 · 2018: 19 · 2017: 9 · 2016: 1
FWCI
71.33
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
  • ARCA (Università Ca' Foscari Venezia) (submittedVersion, cc-by)
  • arXiv (Cornell University)
Funders
European Commission (317532, 610704)
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Communication › Social Media and Politics
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
echo chambers, emotional contagion, group polarization, Facebook, Gompertz model, logistic growth model, log-logistic model, user engagement, scientific content, social media communities
MeSH terms
Communication, Emotions, Humans, Italy, Language, Risk, Social Behavior, Least-Squares Analysis, Social Networking, Social Media

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review