Computational Fact Checking from Knowledge Networks

By Giovanni Luca Ciampaglia, Prashant Shiralkar, Luis Mateus Rocha, Johan Bollen, Filippo Menczer, Alessandro Flammini. PLoS ONE, 2015

477 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/c3a548efe0703043c69f3d2f988e15f406b26ba8

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Citations in the corpus

Echo Chambers: Emotional Contagion and Group Polarization on Facebook (2016, background): “16,17 To make things more complicated, users on social media aim at maximizing the number of likes (Attention Bulimia) and often information, concepts, and debate get ﬂattened and oversimpliﬁed.”
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking (2017, background): “Prior computational works (Vlachos and Riedel, 2014; Ciampaglia et al., 2015) have proposed fact-checking through entailment from knowledge bases.”
Fake News Detection on Social Media: A Data Mining Perspective (2017, background): “Factchecking using a knowledge graph aims to check whether the claims in news content can be inferred from existing facts in the knowledge graph [98; 16; 72].”
Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, methodology): “…the edge labeled Predicate to exist from the node representing Subject to the one representing Object can be computed, e.g., using link prediction methods such as semantic proximity [Ciampaglia et al. 2015], discriminative predicate path [Shi and Weninger 2016], or LinkNBed [Trivedi et al. 2018].”
A Survey of Fake News (2020, methodology): “Based on the preceding representation of knowledge, we present the following widely accepted definitions for the key terms that often appear in automatic fact-checking literature for a better understanding [Ciampaglia et al. 2015; Dong et al. 2014; Shi and Weninger 2016].”
A Survey on Application of Knowledge Graph (2020, background): “Fake news detection problem is viewed as a link-prediction task in a KG by the research work [49].”
SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums (2019, methodology): “For instance, Ciampaglia et al. (2015) do this using a knowledge graph derived from Wikipedia.”
dEFEND: Explainable Fake News Detection (2019, background): “Researchers also attempt to use external sources to fact-check the claims in news articles to decide and explain whether a news piece is fake or not [6], which may not be able to check newly emerging events (that has not been fact-checked).”
ProjE: Embedding Projection for Knowledge Graph Completion (2016, background): “4 show that ProjE outperforms existing fact checking and link prediction models [2, 6, 17, 9, 15, 22] in all but one question type.”
ExFaKT: A Framework for Explaining Facts over Knowledge Graphs and Text (2019, background): “, [2, 9, 32, 33]) estimate the support for factual statements by mining and ranking connectivity patterns in KGs.”
A survey on fake news and rumour detection techniques (2019, methodology): “More specifically, the use of Wikipedia info-boxes to generate a knowledge graph has been proposed in [23].”
Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster (2017, methodology): “[4] proposed a method for fact-checking using knowledge graphs by finding the shortest path between entity nodes.”
Hoaxy: A Platform for Tracking Online Misinformation (2016, background): “Automatic attempts to perform fact checking have been recently proposed for simple statements [11], and for multimedia content [6].”