The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Hoaxy: A Platform for Tracking Online Misinformation
Open accessAbstract
Massive amounts of misinformation have been observed to spread in uncontrolled fashion across social media. Examples include rumors, hoaxes, fake news, and conspiracy theories. At the same time, several journalistic organizations devote significant efforts to high-quality fact checking of online claims. The resulting information cascades contain instances of both accurate and inaccurate information, unfold over multiple time scales, and often reach audiences of considerable size. All these factors pose challenges for the study of the social dynamics of online news sharing. Here we introduce Hoaxy, a platform for the collection, detection, and analysis of online misinformation and its related fact-checking efforts. We discuss the design of the platform and present a preliminary analysis of a sample of public tweets containing both fake news and fact checking. We find that, in the aggregate, the sharing of fact-checking content typically lags that of misinformation by 10-20 hours. Moreover, fake news are dominated by very active users, while fact checking is a more grass-roots activity. With the increasing risks connected to massive online misinformation, social news observatories have the potential to help researchers, journalists, and the general public understand the dynamics of real and fake news sharing.
Overview
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.
Integrity signals
Registry
Cites
Statistics
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Birds of a Feather: Homophily in Social Networks
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001) · Annual Review of Sociology · doi:10.1146/annurev.soc.27.1.415 · Cited by 18,567
Users of the world, unite! The challenges and opportunities of Social Media
Kaplan, A. M., & Haenlein, M. (2010) · Business Horizons · doi:10.1016/j.bushor.2009.09.003 · Cited by 17,875
The Wealth of Networks: How Social Production Transforms Markets and Freedom
Benkler, Y. (2017) · Yale University Press eBooks · doi:10.12987/9780300127232 · Cited by 3,680
Wikinomics: how mass collaboration changes everything
(2007) · Choice Reviews Online · doi:10.5860/choice.44-6933 · Cited by 3,367
Exposure to ideologically diverse news and opinion on Facebook
Bakshy, E., Messing, S., & Adamic, L. A. (2015) · Science · doi:10.1126/science.aaa1160 · Cited by 3,245
Information credibility on twitter
Castillo, C., Mendoza, M., & Poblete, B. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963500 · Cited by 2,593
The 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
Political Polarization on Twitter
Conover, M., Ratkiewicz, J., Francisco, M., Goncalves, B., Flammini, A., & Menczer, F. (2011) · Zenodo · doi:10.5281/zenodo.4589064 · Cited by 1,254
Self-citationKata, A. (2012) · Vaccine · doi:10.1016/j.vaccine.2011.11.112 · Cited by 1,094 · cited 1×
“The worrying consequences in the offline world [8, 22, 29, 23, 27, 7]; examples include rumors [18], false news [8, 23], hoaxes, and Hurricane Sandy, false reports that the New York Stocks Exchange had been flooded were corrected in less than an hour [36].”
BackgroundPolitical Polarization on Twitter
Conover, M., Ratkiewicz, J., Francisco, M., Goncalves, B., Menczer, F., & Flammini, A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14126 · Cited by 626 · cited 1×
“…features of the information exchange networks underlying social media create peculiar patterns of information access. homophily [25], polarization [12], algorithmic ranking [3], and social bubbles [28] — information environments with these challenges, here we present Hoaxy , an upcoming platform…”
BackgroundSelf-citation
Cited by
Fake News Detection on Social Media: A Data Mining Perspective
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3137597.3137600 · Cited by 3,304
Fake news on Twitter during the 2016 U.S. presidential election
Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019) · Science · doi:10.1126/science.aau2706 · Cited by 1,803
Tucker, J. A., Guess, A., Barbera, P., Vaccari, C., Siegel, A., Sanovich, S., Stukal, D., & Nyhan, B. (2018) · doi:10.2139/ssrn.3144139 · Cited by 1,222
A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
Zhou, X., & Zafarani, R. (2021) · ACM Computing Surveys · doi:10.1145/3395046 · Cited by 1,184
An overview of online fake news: Characterization, detection, and discussion
Zhang, X., & Ghorbani, A. A. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2019.03.004 · Cited by 997
Metadata
- Authors
- Shao, C., Ciampaglia, G. L., Flammini, A., & Menczer, F.
- Venue
- Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion
- Publisher
- ACM Press
- Published
- 2016
- Pages
- 745-750
- OpenAlex
- W2296706733
- Cited by
- 389
- References
- 32
- FWCI
- 147.62
- Type
- conference paper
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Hoaxy: A Platform for Tracking Online Misinformation. https://app.cochat.ai/papers/10.1145/2872518.2890098
- The paper
- Shao, C., Ciampaglia, G. L., Flammini, A., & Menczer, F. (2016). Hoaxy: A Platform for Tracking Online Misinformation. Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion. https://doi.org/10.1145/2872518.2890098
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
