Computing the Veracity of Information through Crowds: A Method for Reducing the Spread of False Messages on Social Media
Open accessAbstract
Twitter and other social media allow people to post their experiences and opinions online. This information can be useful for making informed decisions. However, people can unintentionally spread false information. The work reported here focused on examining how to reduce the spread of inaccurate information on social media. In particular, we examined the effect of collective opinion on information forwarding in social media environments through an experiment with crowds. In Twitter, an indicator of collective opinion is the number of people who have retweeted a message. The results showed that displaying both retweet counts and collective truthfulness ratings could reduce the spread of inaccurate health-related messages. This finding suggests that collecting and displaying the truthfulness ratings of crowds in addition to their forwarding decisions can reduce the spread of false information on social media.
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When no authoritative statement confirms or denies a rumour, social media users often share their own views on its truth through collective sense-making, which can expose the truth.
“In the absence of an authoritative statement corroborating or debunking an ongoing rumour, it is observed that social media users will often share their own thoughts on its veracity via a process of collective, inter-subjective sense-making”
From Detection and Resolution of Rumours in Social Media (2019) · cites this paper as Li and Sakamoto 2015
Social media's capacity to aggregate many users' judgements motivates more machine learning research on rumour classification systems.
“The ability of social media to aggregate the judgements of a large community of users (Li and Sakamoto 2015) thus motivates further study of machine learning approaches to improve rumour classification systems.”
From Detection and Resolution of Rumours in Social Media (2019) · cites this paper as Li and Sakamoto 2015
Cites
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“Another online experiment showed that the number of times a piece of music was downloaded in the past could predict its future popularity when the number of downloads was available to the users [27].”
BackgroundConsumers' Need for Uniqueness: Scale Development and Validation
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“For example, the spread of unverified information that turned out to be false could be far reaching during and after a disaster [16, 18, 31].”
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Systematic Literature Review on the Spread of Health-related Misinformation on Social Media
Wang, Y., McKee, M., Torbica, A., & Stuckler, D. (2019) · Social Science & Medicine · doi:10.1016/j.socscimed.2019.112552 · Cited by 1,806 · see citing works
Detection and Resolution of Rumours in Social Media
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“The ability of social media to aggregate the judgements of a large community of users [Li and Sakamoto 2015] thus motivates further study of machine learning approaches to improve rumour classification systems.”
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Alsubaei, F. S. (2023) · Applied Sciences · doi:10.3390/app13053013 · Cited by 9 · see citing works
- Authors
- Li, H., & Sakamoto, Y.
- Affiliations
- Stevens Institute of Technology (US)
- ORCID
- Li, Huaye
- Venue
- 2015 48th Hawaii International Conference on System Sciences
- Publisher
- IEEE
- Published
- 2015-01
- Pages
- 2003-2012
- ISSN
- 1530-1605 (ISSN-L)
- OpenAlex
- W3125113962
- Cited by
- 22
- References
- 5
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 4 · 2022: 1 · 2021: 4 · 2020: 3 · 2019: 2 · 2018: 2 · 2014: 1
- FWCI
- 2.43
- Type
- conference paper
- Language
- en
- Open access
- green
- doi.org
- Locations
- Hawaii International Conference on System Sciences (HICSS) (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- misinformation, social media, Twitter, crowds, collective opinion, health misinformation, data forwarding, crowdsourcing
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