Polarization and Fake News: Early Warning of Potential Misinformation Targets
Open accessAbstract
Users’ polarization and confirmation bias play a key role in misinformation spreading on online social media. Our aim is to use this information to determine in advance potential targets for hoaxes and fake news. In this article, we introduce a framework for promptly identifying polarizing content on social media and, thus, “predicting” future fake news topics. We validate the performances of the proposed methodology on a massive Italian Facebook dataset, showing that we are able to identify topics that are susceptible to misinformation with 77% accuracy. Moreover, such information may be embedded as a new feature in an additional classifier able to recognize fake news with 91% accuracy. The novelty of our approach consists in taking into account a series of characteristics related to users’ behavior on online social media such as Facebook, making a first, important step towards the mitigation of misinformation phenomena by supporting the identification of potential misinformation targets and thus the design of tailored counter-narratives.
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Online social network structures, governed by social homophily, can lead to selective exposure and creation of homogeneous echo chambers and polarization of opinions, which may amplify or dampen the spread of misinformation among certain demographics.
“Online social network structures, governed by social homophily, can lead to selective exposure and creation of homogeneous echo chambers 52,53 and polarization of opinions, which may amplify (or dampen) the spread of misinformation among certain demographics.”
From Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA (2021) · cites this paper as [53]
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Experimental — accuracy not yet measuredOnline polarization may foster misinformation spreading.
“Online polarization, for instance, may foster misinformation spreading (1, 8).”
From The echo chamber effect on social media (2021) · cites this paper as [8]
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References 62 · Cited by 280
Research works printed but not linked: 4
- O. Dictionaries, "Oxford dictionaries word of the year 2016 is...post-truth," Website, 2017. [Online]. Available: https://www.oxforddictionaries.com/press/news/2016/12/11/WOTY-16
- S. Kumar, M. Jiang, T. Jung, R. J. Luo, and J. Leskovec, "Mis2: Misinformation and misbehavior mining on the web, " in Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM, 2018, pp. 799-800.
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- Authors
- Vicario, M. D., Quattrociocchi, W., Scala, A., & Zollo, F.
- Affiliations
- IMT School for Advanced Studies Lucca (IT)
- Ca' Foscari University of Venice (IT)
- ISC-CNR Sapienza University of Rome, Rome, Italy
- Venue
- ACM Transactions on the Web
- Published
- 2019-03-27
- Volume
- 13
- Issue
- 2
- Pages
- 1-22
- ISSN
- 1559-1131 (ISSN-L)
- 1559-114X
- OpenAlex
- W2963877803
- Cited by
- 280
- References
- 62
- Citations by year
- 2026: 17 · 2025: 33 · 2024: 34 · 2023: 42 · 2022: 59 · 2021: 46 · 2020: 35 · 2019: 8 · 2018: 5 · 2013: 1
- FWCI
- 74.46
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- ACM Transactions on the Web (publishedVersion)
- ARCA (Università Ca' Foscari Venezia) (submittedVersion, other-oa)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Funders
- AMOFI (P0000326)
- IMT/Extrapola Srl project
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- fake news, misinformation, polarization, confirmation bias, hoaxes, social media, Facebook, user behavior, counter-narratives, misinformation mitigation
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