Computational Fact Checking from Knowledge Networks

Ciampaglia, Giovanni Luca; Shiralkar, Prashant; Rocha, Luís M.; Bollen, Johan; Menczer, Filippo; Flammini, Alessandro

doi:10.1371/journal.pone.0128193

Cited by 349 publications

(283 citation statements)

References 36 publications

(48 reference statements)

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“…To counteract this trend, algorithmic-driven solutions have been proposed (24)(25)(26)(27)(28)(29), e.g., Google (30) is developing a trustworthiness score to rank the results of queries. Similarly, Facebook has proposed a community-driven approach where users can flag false content to correct the newsfeed algorithm.…”

mentioning

confidence: 99%

The spreading of misinformation online

Vicario

Bessi

Zollo

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

1,626

981

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The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web (WWW) also allows for the rapid dissemination of unsubstantiated rumors and conspiracy theories that often elicit rapid, large, but naive social responses such as the recent case of Jade Helm 15--where a simple military exercise turned out to be perceived as the beginning of a new civil war in the United States. In this work, we address the determinants governing misinformation spreading through a thorough quantitative analysis. In particular, we focus on how Facebook users consume information related to two distinct narratives: scientific and conspiracy news. We find that, although consumers of scientific and conspiracy stories present similar consumption patterns with respect to content, cascade dynamics differ. Selective exposure to content is the primary driver of content diffusion and generates the formation of homogeneous clusters, i.e., "echo chambers." Indeed, homogeneity appears to be the primary driver for the diffusion of contents and each echo chamber has its own cascade dynamics. Finally, we introduce a data-driven percolation model mimicking rumor spreading and we show that homogeneity and polarization are the main determinants for predicting cascades' size. misinformation | virality | Facebook | rumor spreading | cascades

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mentioning

confidence: 99%

The spreading of misinformation online

Vicario

Bessi

Zollo

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

1,626

981

View full text Add to dashboard Cite

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“…In [11], authors consider claims as RDF triples, the reference source a knowledge graph such as DBPedia, and cast the task of assessing claim accuracy as a problem of finding short paths in the knowledge graph that connect the claim subject to its object. A truth (or support) value is assigned to each such path, taking into account not only the graph proximity but also the generality of the entities encountered along the path.…”

Section: Claim Accuracy Assessmentmentioning

confidence: 99%

A Content Management Perspective on Fact-Checking

Cazalens

Lamarre

Leblay³

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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Fact checking has captured the attention of the media and the public alike; it has also recently received strong attention from the computer science community, in particular from data and knowledge management, natural language processing and information retrieval; we denote these together under the term "content management". In this paper, we identify the fact checking tasks which can be performed with the help of content management technologies, and survey the recent research works in this area, before laying out some perspectives for the future. We hope our work will provide interested researchers, journalists and fact checkers with an entry point in the existing literature as well as help develop a roadmap for future research and development work.

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“…These include: news gathering, verification, and delivery of corrections [42][43][44][45][46]. These activities are already capitalizing on the growing number of tools, data sets, and platforms contributed by computer scientists to detect, define, model, and counteract the spread of misinformation [47][48][49][50][51][52][53][54][55][56].…”

Section: A Call To Action For Computational Social Scientistsmentioning

confidence: 99%

Fighting fake news: a role for computational social science in the fight against digital misinformation

Ciampaglia

2017

J Comput Soc Sc

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The massive, uncontrolled, and oftentimes systematic spread of inaccurate and misleading information on the Web and social media poses a major risk to society. Digital misinformation thrives on an assortment of cognitive, social, and algorithmic biases and current countermeasures based on journalistic corrections do not seem to scale up. By their very nature, computational social scientists could play a twofold role in the fight against fake news: first, they could elucidate the fundamental mechanisms that make us vulnerable to misinformation online and second, they could devise effective strategies to counteract misinformation.

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Computational Fact Checking from Knowledge Networks

Cited by 349 publications

References 36 publications

The spreading of misinformation online

The spreading of misinformation online

A Content Management Perspective on Fact-Checking

Fighting fake news: a role for computational social science in the fight against digital misinformation

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