Tweet, but verify: epistemic study of information verification on Twitter
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Zubiaga and Ji (2014) found that crowd workers' verification work tends to favour selecting true labels for inaccurate information.
“This is what Zubiaga and Ji (2014) found in their study, suggesting that verification work performed by crowd workers tends to favour selection of true labels for inaccurate information.”
From Detection and Resolution of Rumours in Social Media (2019) · cites this paper as Zubiaga and Ji 2014
Credibility perceptions have been annotated in prior work to determine how credible claims appear to people; this could help identify misleading rumours but has not yet been applied to rumours.
“Annotation of credibility perceptions has also been done in previous work, determining how credible claims appear to people (Mitra and Gilbert 2015;Zubiaga and Ji 2014); this could be useful in the context of rumours to identify those that are likely to be misleading for people; however, it has not yet been applied in the context of rumours.”
From Detection and Resolution of Rumours in Social Media (2019) · cites this paper as Zubiaga and Ji 2014
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Positive Information Diffusion for Rumor Containment in Online Social Networks
Tripathi, R., & Rao, S. (2020) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets48256.2020.9027478 · Cited by 9 · see citing works
TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision
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Rumor containment in peer-to-peer message sharing online social networks
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RPI-BLENDER TAC-KBP2013 Knowledge Base Population System
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Self-citationMeinert, J., Aker, A., & Krämer, N. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.313 · Cited by 7 · see citing works
Feng, B. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1351820 · Cited by 8 · see citing works
Analysis of Online Toxicity Detection Using Machine Learning Approaches
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Using Location-Based Social Networks for Time-Constrained Information Dissemination
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Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era
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Costly Displays in a Digital World: Signalling Trustworthiness on Social Media
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Integrating Content Authentication Support in Media Services
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Social Media Veracity Detection System Using Calibrate Classifier
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Fake News and SDG16: The Situation in Ghana
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The brand persona:operationalizing a synthesis of brand equity and social capital
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Mining Social Media for Newsgathering.Preprint
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Self-citationIn Search of Credible NewsPreprint
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Automatic Detection of Disinformation: A Systematic Mapping Study
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- Authors
- Zubiaga, A., & Ji, H.
- Affiliations
- Queens College, CUNY (US)
- Rensselaer Polytechnic Institute (US)
- Corresponding author
- Zubiaga, Arkaitz
- Venue
- Social Network Analysis and Mining
- Published
- 2014-03-25
- Volume
- 4
- Issue
- 1
- ISSN
- 1869-5450 (ISSN-L)
- 1869-5469
- OpenAlex
- W2110785023
- Cited by
- 125
- References
- 42
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 10 · 2023: 5 · 2022: 15 · 2021: 15 · 2020: 16 · 2019: 14 · 2018: 15 · 2017: 10 · 2016: 7 · 2015: 9 · 2014: 1 · 2013: 1
- FWCI
- 29.31
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Social Network Analysis and Mining (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Warwick Research Archive Portal (University of Warwick) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (CAREER, W911NF-09-2-0053, 0953149)
- Rensselaer Polytechnic Institute
- Defense Advanced Research Projects Agency (FA8750-13-2-0041, FA8750, W911NF-09-2-0053)
- Army Research Laboratory (Cooperative Agreement No. W911NF-09-2-0053, W911NF, No. W911NF-09-2-0053, W911NF-09-2-0053)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Public Relations and Crisis Communication
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- fact-checking, Twitter, perceived credibility, hoaxes, misinformation, Hurricane Sandy, epistemology, trust in information, corroboration, author verification, natural disasters
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 ›
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