Measuring social response to different journalistic techniques on Facebook
Open accessAbstract
Recent studies have shown that online users tend to select information that adheres to their system of beliefs, ignore information that does not, and join groups that share a common narrative. This information environment can elicit tribalism instead of informed debate, especially when issues are controversial. Algorithmic solutions, fact-checking initiatives, and many other approaches have shown limitations in dealing with this phenomenon, and heated debate and polarization still play a pivotal role in online social dynamics (e.g. traditional vs. anti-establishment polarization). To understand the effect of different communication strategies able to smooth polarization, in this paper, together with Corriere della Sera, a major Italian news outlet, we measure the social response of users to different types of news framing. We analyse users’ reactions to 113 ad-hoc articles published on the newspaper’s Facebook page and the corresponding news articles on the topic of migration, published from March to December 2018. We examine different journalistic techniques and content types by analyzing their impact on user comments in terms of toxicity, criticism of the newspaper, and stance concerning migration. We find that visual pieces and factual news reports elicit the highest level of trust in the media source, while opinion pieces and editorials are more likely to be criticized. We also notice that data-driven pieces elicit an extremely low level of trust in the news source. Furthermore, coherently with the echo chambers behaviour, we find social conformity strongly affecting the commenting behaviour of users on Facebook.
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Users in anti-vaccination communities tend to exclusively consume and share polarized content, either supporting or opposing vaccinations.
“Schmidt et al. [66] analyzed anti-vaccination communities and found that users tend to exclusively consume and share polarized content, either supporting or opposing vaccinations.”
From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [66]
The Introduction says online users tend to select belief-consistent content and form like-minded groups, supporting the general point about selective consumption. But the Discussion says this paper analyzes attitudes toward migration, not anti-vaccination communities. Our copy does not establish exclusive consumption or sharing of pro- or anti-vaccination content. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe amount of media a person consumes is anti-proportional to their chance of being trapped in an echo chamber, which contradicts findings by CSS studies that engagement and echo chambers are positively correlated.
“They find that the amount of media a person consumes is anti-proportional to their chance of being trapped in an echo chamber, which contradicts findings by CSS studies [48,50,66,67] that find that engagement and echo chambers are positively correlated.”
From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [66]
The Introduction says users continue selecting belief-consistent content as they enter echo chambers, supporting a qualitative link between content selection and echo-chamber behavior. The Results examine news framing and commenting, not whether greater engagement or media consumption is positively correlated with being in an echo chamber. The inverse relationship is attributed to a different study in the citing sentence, not to this source. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe majority of studies has found evidence for echo chambers on platforms such as Facebook.
“Although the majority of studies has found evidence for echo chambers on platforms such as Facebook (e.g., [66]), there is still a substantial amount of studies that had mixed findings, and one-fourth of studies that focused on the echo chamber hypothesis have not identified evidence for echo chambers (e.g., [74,129]).”
From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [66]
The Introduction says recent studies find that users cluster with like-minded people in echo chambers. The Abstract also reports Facebook commenting behavior consistent with echo chambers. Neither passage establishes that a majority of studies found such evidence. (LLM, conf=0.84)
Experimental — accuracy not yet measured
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Cites
The spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847 · cited 1× · see citing works
“The result has been a loss of trust in traditional media (Authority for Communications Guarantees, 2018), and a growing interest in investigating the spread of fake news (Del Vicario et al., 2016; Lazer et al., 2018; Vosoughi et al., 2018).”
BackgroundThe spreading of misinformation online
Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517441113 · Cited by 2,407 · see citing works
Self-citationExposure to opposing views on social media can increase political polarization
Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. B. F., Lee, J., Mann, M., Merhout, F., & Volfovsky, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804840115 · Cited by 1,863 · see citing works
Logistic Regression: A Brief Primer
Stoltzfus, J. C. (2011) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.2011.01185.x · Cited by 1,290 · see citing works
Gupta, A., Lamba, H., Kumaraguru, P., & Joshi, A. (2013) · Proceedings of the 22nd International Conference on World Wide Web Companion · doi:10.1145/2487788.2488033 · Cited by 573 · cited 1× · see citing works
“Efforts to limit the spread of fake news have ranged from debunking and fact-checking initiatives (Silverman, 2015; The Poynter Institute, 2019), to algorithmic-based solutions (Zhang et al., 2016, 2019; Castillo et al., 2011; Gupta et al., 2013; Kwon et al., 2013).”
BackgroundDebunking in a world of tribes
Zollo, F., Bessi, A., Del Vicario, M., Scala, A., Caldarelli, G., Shekhtman, L., Havlin, S., & Quattrociocchi, W. (2017) · PLOS ONE · doi:10.1371/journal.pone.0181821 · Cited by 345 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 18
- Bessi A, Coletto M, Davidescu GA, Scala A, Caldarelli G, Quattrociocchi W (2015) Science vs conspiracy: collective narratives in the age of misinformation. PLoS ONE 10(2):e0118093
- Castillo C, Mendoza M, Poblete B (2011) Information credibility on twitter. In: Proceedings of the 20th international conference on world wide web. ACM, pp. 675-684
- Cohen J (2013) Statistical power analysis for the behavioral sciences. Routledge
- Deutsch M, Gerard HB (1955) A study of normative and informational social influences upon individual judgment. J Abnorm Soc Psychol 51(3):629
- Devineni P, Koutra D, Faloutsos M, Faloutsos C (2017) Facebook wall posts: a model of user behaviors. Soc Netw Anal Min 7(1):6
- Kwon S, Cha M, Jung K, Chen W, Wang Y (2013) Prominent features of rumor propagation in online social media. In: Proceedings of the 2013 IEEE 13th international conference on data mining. IEEE, pp. 1103-1108
- Lazer DM, Baum MA, Benkler Y, Berinsky AJ, Greenhill KM, Menczer F, Metzger MJ, Nyhan B, Pennycook G, Rothschild D, Schudson M (2018) The science of fake news. Science 359(6380):1094-1096
- Leon AC (1998) Descriptive and inferential statistics. In: Schooler NR (ed) Com- prehensive clinical psychology, vol. 3. Elsevier Science, pp. 243-285
- Lin M, Lucas Jr HC, Shmueli G (2013) Research commentary-too big to fail: large samples and the p-value problem. Inf Syst Res 24(4):906-917
- McKight PE, Najab J (2010) Kruskal-Wallis test. In: Weiner IB, Craighead WE (eds) The Corsini Encyclopedia of Psychology, https://doi.org/10.1002/ 9780470479216. · DOI
- corpsy0491 · DOI
- Quattrociocchi W (2017) Inside the echo chamber. Sci Am 316(4):60-63
- Ruths D (2019) The misinformation machine. Science 363(6425):348-348
- Schmidt AL, Zollo F, Del Vicario M, Bessi A, Scala A, Caldarelli G, Stanley HE, Quattrociocchi W (2017) Anatomy of news consumption on Facebook. Proc Natl Acad Sci USA 114(12):3035-3039
- Swire-Thompson B, Ecker UK, Lewandowsky S, Berinsky AJ (2020) They might be a liar but they're my liar: source evaluation and the prevalence of mis- information. Pol Psychol 41(1):21-34
- Vicario MD, Quattrociocchi W, Scala A, Zollo F (2019) Polarization and fake news: Early warning of Potential misinformation targets. ACM Trans Web (TWEB) 13(2):10
- Zhang H, Kuhnle A, Zhang H, Thai MT (2016) Detecting misinformation in online social networks before it is too late. In: Proceedings of the 2016 IEEE/ACM international conference on advances in social networks analysis and mining. IEEE Press, pp. 541-548
- Zhang Q, Lipani A, Liang S, Yilmaz E (2019) Reply-aided detection of mis- information via Bayesian deep learning. In: The world wide web conference. ACM, pp. 2333-2343
Cited by
Sorted by citations
Hartmann, D., Wang, S. M., Pohlmann, L., & Berendt, B. (2025) · Journal of Computational Social Science · doi:10.1007/s42001-025-00381-z · Cited by 59 · see citing works
- Authors
- Schmidt, A. L., Peruzzi, A., Scala, A., Cinelli, M., Pomerantsev, P., Applebaum, A., Gaston, S., Fusi, N., Peterson, Z., Severgnini, G., De Cesco, A. F., Casati, D., Novak, P. K., Stanley, H. E., Zollo, F., & Quattrociocchi, W.
- Affiliations
- Ca' Foscari University of Venice (IT)
- London School of Economics and Political Science (GB)
- Jožef Stefan Institute (SI)
- ORCID
- Venue
- Humanities and Social Sciences Communications
- Published
- 2020-07-01
- Volume
- 7
- Issue
- 1
- ISSN
- 2055-1045 (ISSN-L)
- 2662-9992
- OpenAlex
- W3040624321
- Cited by
- 10
- References
- 6
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 2 · 2020: 2
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Update notices
- correction (2020-08-14)
- Open access
- gold
- doi.org
- Locations
- Humanities and Social Sciences Communications (publishedVersion, cc-by)
- London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion)
- London School of Economics and Political Science Research Online (London School of Economics and Political Science) (submittedVersion)
- RePEc: Research Papers in Economics (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)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Media Studies and Communication
- Social Sciences › Communication › Journalism and Media Studies
- Keywords
- journalistic techniques, news framing, Facebook comments, social response, polarization, echo chambers, tribalism, migration news, media trust, opinion articles, data journalism, conformity, online comments, Corriere della Sera
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