CoChat
Sign Up
Cited by 486Misinformation and Its ImpactsComplex Network Analysis TechniquesTopic Modeling

Computational Fact Checking from Knowledge Networks

Open access
Ciampaglia, Giovanni Luca, Shiralkar, Prashant, Rocha, Luis M., Bollen, Johan, Menczer, Filippo, Flammini, Alessandro·2015·PLOS ONE
DOI 10.1371/journal.pone.0128193PMC PMC4471100
Open this paper in CoChat — free accountOpen PDF

Abstract

Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pone.0128193

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

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.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Individual rumors are debunked in due time by fact-checking measures, but public perception about anthropogenic climate change has remained controversial for a long time.

    “Individual rumors are being debunked in due time by fact checking measures (Zhao et al., 2015;Ciampaglia et al., 2015), however, public perception about some issues like human induced (anthropogenic) climate change have remained controversial for a long time (Tvinnereim et al., 2017).”

    From Credibility of climate change denial in social media (2019) · cites this paper as Ciampaglia et al., 2015

    The Introduction says that vandalism, distortions, and outright lies on Wikipedia are frequently repaired within minutes. That supports timely correction of some false information, but not the broader claim that individual rumors are being debunked by fact-checking measures. The Discussion describes computational fact-checking as a potential way to mitigate misinformation, not an established record of rumor debunking. The climate-change clause is attributed to a different citation. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 35

  1. [1]Mendoza M, Poblete B, Castillo C. Twitter Under Crisis: Can We Trust What We RT? In: Proceedings of the First Workshop on Social Media Analytics SOMA’10. New York, NY, USA: ACM; 2010 p. 71– 79.
  2. [2]Ratkiewicz J, Conover M, Meiss M, GonÇalves B, Flammini A, Menczer F. Detecting and Tracking Political Abuse in Social Media In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. Barcelona, Spain: AAAI; 2011.
  3. [3]Cranor LF, LaMacchia BA. Spam! Commun ACM. 1998 8; 41(8): 74– 83. 10.1145/280324.280336 · DOI
  4. [4]Jagatic TN, Johnson NA, Jakobsson M, Menczer F. Social Phishing. Commun ACM. 2007 10; 50(10): 94– 100. 10.1145/1290958.1290968 · DOI · Paper page
  5. [5]Friggeri A, Adamic LA, Eckles D, Cheng J. Rumor Cascades. In: Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media. Ann Arbor, MI: AAAI; 2014.
  6. [6]Flanagin AJ, Metzger MJ. Perceptions of Internet Information Credibility. Journalism & Mass Communication Quarterly. 2000; 77(3): 515– 540. 10.1177/107769900007700304 · DOI
  7. [9]Castillo C, Mendoza M, Poblete B. Information credibility on Twitter In: Proceedings of the 20th international conference on World Wide Web. Hydebarad, India: ACM; 2011 p. 675.
  8. [11]Wilner T. Meet the robots that factcheck. Columbia Journalism Review. 2014 September/October;.
  9. [12]Gupta A, Kumaraguru P, Castillo C, Meier P. TweetCred: A Real-time Web-based System for Assessing Credibility of Content on Twitter In: Proc. 6th International Conference on Social Informatics (SocInfo). Barcelona, Spain; 2014.
  10. [13]Resnick P, Carton S, Park S, Shen Y, Zeffer N. RumorLens: A System for Analyzing the Impact of Rumors and Corrections in Social Media. In: Proc. Computational Journalism Conference; 2014.
  11. [14]Wu Y, Agarwal PK, Li C, Yang J, Yu C. Toward Computational Fact-Checking In: Proceedings of the VLDB Endowment. vol. 7 Hangzhou, China; 2014.
  12. [15]Finn S, Metaxas PT, Mustafaraj E, O’Keefe M, Tang L, Tang S, et al. TRAILS: A System for Monitoring the Propagation of Rumors On Twitter. In: Proc. Computational Journalism Conference; 2014.
  13. [16]Berners-Lee T, Hendler J, Lassila O. The Semantic Web. Scientific American. 2001; 284(5): 28– 37. 10.1038/scientificamerican0501-34 · DOI
  14. [18]Priedhorsky R, Chen J, Lam STK, Panciera K, Terveen L, Riedl J. Creating, Destroying, and Restoring Value in Wikipedia. In: Proceedings of the 2007 International ACM Conference on Supporting Group Work GROUP’07. New York, NY, USA: ACM; 2007 p. 259– 268.
  15. [19]DeDeo S. Collective Phenomena and Non-Finite State Computation in a Human Social System. PLoS ONE. 2013; 8(10): e75818 10.1371/journal.pone.0075818 24130745 PMC3794014 · DOI
  16. [20]Cohen S, Hamilton JT, Turner F. Computational Journalism. Commun ACM. 2011 10; 54(10): 66– 71. 10.1145/2001269.2001288 · DOI
  17. [22]Nickel M, Murphy K, Tresp V, Gabrilovich E. A Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction. arXiv preprint arXiv: 150300759 2015;.
  18. [23]Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, Ives Z. DBpedia: A Nucleus for a Web of Open Data In: Aberer K, Cudré-Mauroux P, Choi KS, Noy N, Allemang D, Lee KI, et al, editors. The Semantic Web. vol. 4825 of Lecture Notes in Computer Science. Springer Berlin Heidelberg; 2007 p. 722– 735.
  19. [24]Dong X, Gabrilovich E, Heitz G, Horn W, Lao N, Murphy K, et al Knowledge Vault: A Web-scale Approach to Probabilistic Knowledge Fusion. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD’14. New York, NY, USA: ACM; 2014 p. 601– 610.
  20. [25]Etzioni O, Banko M, Soderland S, Weld DS. Open Information Extraction from the Web. Commun ACM. 2008 12; 51(12): 68– 74. 10.1145/1409360.1409378 · DOI
  21. [26]Niu F, Zhang C, Ré C, Shavlik J. Elementary: Large-scale knowledge-base construction via machine learning and statistical inference. International Journal on Semantic Web and Information Systems. 2012; 8(3): 42– 73. 10.4018/jswis.2012070103 · DOI
  22. [27]Simas T, Rocha LM. Distance Closures on Complex Networks Network Science. 2014;p. In Press. (arXiv:1312.2459). · arXiv
  23. [28]Markines B, Menczer F. A Scalable, Collaborative Similarity Measure for Social Annotation Systems. In: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia HT’09. New York, NY, USA: ACM; 2009 p. 347– 348.
  24. [29]Aiello L, Barrat A, Schifanella R, Cattuto C, Markines B, Menczer F. Friendship prediction and homophily in social media. ACM Trans WEB. 2012; 6(2): 9 10.1145/2180861.2180866 · DOI
  25. [30]Lao N, Mitchell T, Cohen WW. Random Walk Inference and Learning in a Large Scale Knowledge Base. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing EMNLP’11. Stroudsburg, PA, USA: Association for Computational Linguistics; 2011 p. 529– 539.
  26. [31]Adamic LA, Glance N. The Political Blogosphere and the 2004 U.S. Election: Divided They Blog In: Proceedings of the 3rd International Workshop on Link Discovery. ACM, Chicago, Illinois; 2005 p. 36– 43.
  27. [32]Conover M, Ratkiewicz J, Francisco M, GonÇalves B, Flammini A, Menczer F. Political Polarization on Twitter In: Proc. 5th International AAAI Conference on Weblogs and Social Media (ICWSM). Barcelona, Spain: AAAI; 2011.
  28. [33]Kamada T, Kawai S. An algorithm for drawing general undirected graphs. Information Processing Letters. 1989; 31(1): 7– 15. 10.1016/0020-0190(89)90102-6 · DOI
  29. [35]Fawcett T. An Introduction to ROC Analysis. Pattern Recogn Lett. 2006 6; 27(8): 861– 874. 10.1016/j.patrec.2005.10.010 · DOI · Paper page
  30. [37]Masucci AP, Kalampokis A, Eguíluz VM, Hernández-García E. Wikipedia information flow analysis reveals the scale-free architecture of the semantic space. PLoS ONE. 2011; 6(2): e17333 10.1371/journal.pone.0017333 21407801 PMC3046238 · DOI
  31. [38]Capitán JA, Borge-Holthoefer J, Gómez S, Martinez-Romo J, Araujo L, Cuesta JA, et al Local-based semantic navigation on a networked representation of information. PLoS ONE. 2012; 7(8): e43694 10.1371/journal.pone.0043694 22937081 PMC3427177 · DOI
  32. [39]Steyvers M, Tenenbaum JB. The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth. Cognitive Science. 2005; 29(1): 41– 78. 10.1207/s15516709cog2901_3 21702767 · DOI
  33. [40]Nyhan B, Reifler J, Ubel PA. The Hazards of Correcting Myths About Health Care Reform. Medical Care. 2013; 51(2): 127– 132. 10.1097/MLR.0b013e318279486b 23211778 · DOI
  34. [41]Bishop CM. Pattern recognition and machine learning. Springer-Verlag; 2006.
  35. [43]Liben-Nowell D, Kleinberg J. The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology. 2007 5; 58(7): 1019– 1031. 10.1002/asi.20591 · DOI

Cited by

Sorted by citations

Authors
Ciampaglia, G. L., Shiralkar, P., Rocha, L. M., Bollen, J., Menczer, F., & Flammini, A.
Affiliations
Indiana University Bloomington (US)
Corresponding author
Ciampaglia, Giovanni Luca
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2015-06-17
Volume
10
Issue
6
Pages
e0128193
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0128193
PMC
PMC4471100
OpenAlex
W1532503642
Cited by
486
References
5
Citations by year
2026: 12 · 2025: 21 · 2024: 49 · 2023: 35 · 2022: 49 · 2021: 87 · 2020: 73 · 2019: 65 · 2018: 44 · 2017: 29 · 2016: 14 · 2015: 7 · 2013: 1
FWCI
97.49
Type
journal article
Language
en
Update notices
correction (2015-10-27)
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (1101743)
  • U.S. Department of Defense (W911NF1210037)
  • James S. McDonnell Foundation
  • Eli Lilly and Company
  • Lilly Endowment
  • Wikimedia Foundation
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (142353)
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 › Artificial Intelligence › Topic Modeling
Keywords
knowledge graphs, shortest path, semantic proximity, misinformation, Wikipedia, claim verification, fact-checking, misinformation mitigation
MeSH terms
Algorithms, Humans, ROC Curve, Knowledge, Area Under Curve

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