Rumor Propagation is Amplified by Echo Chambers in Social Media
Open accessAbstract
Spreading rumors on the Internet has become increasingly pervasive due to the proliferation of online social media. This paper investigates how rumors are amplified by a group of users who share similar interests or views, dubbed as an echo chamber . To this end, we identify and analyze ‘rumor’ echo chambers , each of which is a group of users who have participated in propagating common rumors. By collecting and analyzing 125 recent rumors from six popular fact-checking sites, and their associated 289,202 tweets/retweets generated by 176,362 users, we find that the rumors that are spread by rumor echo chamber members tend to be more viral and quickly propagated than those that are not spread by echo chamber members. We propose the notion of an echo chamber network that represents relations among rumor echo chambers. By identifying the hub rumor echo chambers (in terms of connectivity to other rumor echo chambers) in the echo chamber network, we show that the top 10% of hub rumor echo chambers contribute to propagation of 24% rumors by eliciting more than 36% of retweets, implying that core rumor echo chambers significantly contribute to rumor spreads.
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A small number of highly connected users are responsible for a significant portion of misinformation spread, with 10% of hub communities generating 36% of retweets of false information.
“For example, Choi et al. [100] found that a small number of highly connected users are responsible for a significant portion of misinformation spread, with 10% of hub communities generating 36% of retweets of false information.”
From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [100]
The Introduction and Results report that the top 10% of highly connected rumor echo chambers contributed to 24% of rumor cascades and elicited more than 36% of retweets. The measured units were groups of users, not individual highly connected users, and the paper describes the content as rumors rather than exclusively verified false information. Page numbers are not provided in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMetrics like polarity scores measure the distance between users based on their attributes when group membership is unavailable.
“Metrics like polarity scores measure the distance between users based on their attributes when group membership is unavailable (e.g., [73,100]).”
From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [100]
The Results section (‘Properties of rumor echo chambers’; page unavailable) describes polarity scores derived from users’ follows and tweets, and a homogeneity score comparing two users’ political positions. It does not present these scores as a way to measure distance when group membership is unavailable; its comparison explicitly distinguishes echo-chamber members from non-members. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMachine learning-based bot detection offers a way to mitigate this issue.
“Machine learning-based bot detection, as proposed by Rusche [110], and implemented by Choi et al. [100] and Gaumont et al. [120], offers a way to mitigate this issue.”
From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [100]
In Methods (Data collection), Choi et al. report using a machine-learning bot detector to identify bot accounts and remove their tweets and retweets when building rumor cascades. This supports the statement that they implemented bot detection, but the paper does not show that it mitigated rumor propagation; the Discussion (Limitation and future work) says detecting and preventing rumor spreads was not covered. (LLM, conf=0.85)
Experimental — accuracy not yet measured
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
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Cited by
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Misinformation: susceptibility, spread, and interventions to immunize the public
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- Authors
- Choi, D., Chun, S., Oh, H., Han, J., & Kwon, T. “.
- Affiliations
- Georgia Institute of Technology (US)
- Seoul National University (KR)
- Sungkyunkwan University (KR)
- Corresponding author
- Han, Jinyoung
- Venue
- Scientific Reports
- Published
- 2020-01-15
- Volume
- 10
- Issue
- 1
- Pages
- 310-310
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6962360
- OpenAlex
- W2999021003
- Cited by
- 164
- References
- 4
- Citations by year
- 2026: 12 · 2025: 23 · 2024: 23 · 2023: 28 · 2022: 36 · 2021: 25 · 2020: 15 · 2019: 1
- FWCI
- 32.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Research Foundation of Korea (NRF-2018R1D1A1A02085647)
- Ministry of Science and ICT is a ministry of the Government of South Korea. It succeeds former Ministry of Science, ICT and Future Planning.
- 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
- rumor propagation, echo chambers, social media, retweets, viral spread, fact-checking, Twitter, misinformation
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