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Cited by 2,593Misinformation and Its ImpactsComplex Network Analysis TechniquesSpam and Phishing Detection

Information credibility on twitter

Castillo, Carlos, Mendoza, Marcelo, Poblete, Barbara·2011·Proceedings of the 20th international conference on World wide web
DOI 10.1145/1963405.1963500
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Abstract

We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors, often unintentionally.

Integrity signals

Registry

Crossref · 10.1145/1963405.1963500

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

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This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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  • Kwon et al. (2013) found random forest performed best for both feature selection and classification, while SVM performed best with baseline features from Castillo et al. (2011).

    “The results showed that for both selection of significant features and subsequent classification, a random forest classifier performed best.The best results using the baseline features adopted from Castillo et al. (2011) were obtained using an SVM.”

    From Detection and Resolution of Rumours in Social Media (2019) · cites this paper as Castillo et al. (2011)

  • Features from Castillo et al. (2011) have subsequently been exploited for veracity classification.

    “Despite the fact that this approach was designed for the classification of credibility perceptions, the features utilised in this work have subsequently also been exploited for veracity classification.”

    From Detection and Resolution of Rumours in Social Media (2019) · cites this paper as Castillo et al. (2011)

  • Liu et al. (2015) proposed verification features based on journalist insights and showed their approach outperformed the Yang et al. (2012) and Castillo et al. (2011) baselines.

    “The authors showed that the proposed approach outperformed the two baselines.”

    From Detection and Resolution of Rumours in Social Media (2019) · cites this paper as Castillo et al. (2011)

  • Yang et al. (2012) reported that adding client-based and location-based features to earlier features substantially boosted accuracy; adding them to Castillo et al. propagation features increased accuracy by 6.3%, from 72.3% to 78.6%.

    “The authors reported that adding these two features on top of earlier reported features led to a substantial boost of accuracy.For instance, adding the two features on top of the propagation-based features reported by Castillo et al. (2011) led to an increase of 6.3% in accuracy (from 72.3% to 78.6%).”

    From Detection and Resolution of Rumours in Social Media (2019) · cites this paper as Castillo et al. (2011)

  • Castillo et al. (2011) initiated research on determining the veracity of social media content, although they studied credibility perceptions rather than rumour veracity.

    “Work by Castillo et al. (2011) initiated research in this direction of determining the veracity of social media content, although the authors did not directly tackle the veracity of rumours but rather their credibility perceptions, i.e., determination of the believability or authority of its source (Zhang et al. 2015).”

    From Detection and Resolution of Rumours in Social Media (2019) · cites this paper as Castillo et al. (2011)

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Authors
Castillo, C., Mendoza, M., & Poblete, B.
Affiliations
  • Yahoo! Research, Barcelona, Spain
  • Universidad Técnica Federico Santa María (CL)
  • Yahoo! Research Latin America and Universidad de Chile, Santiago, Chile
ORCID
Venue
Proceedings of the 20th international conference on World wide web
Publisher
ACM
Published
2011-03-28
Pages
675-684
DOI
10.1145/1963405.1963500
OpenAlex
W2084591134
Cited by
2,593
References
3
Citations by year
2026: 86 · 2025: 151 · 2024: 184 · 2023: 226 · 2022: 237 · 2021: 336 · 2020: 248 · 2019: 229 · 2018: 196 · 2017: 160 · 2016: 115 · 2015: 134 · 2014: 110 · 2013: 93 · 2012: 68
FWCI
251.42
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
International World Wide Web Conference (WWW) (publishedVersion)
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
information credibility, Twitter, microblogging, misinformation, rumor propagation, news credibility, social media

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.

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