Language-Aware Truth Assessment of Fact Candidates

By Ndapandula Nakashole, Tom Michael Mitchell. 2014

108 citations recorded by the source corpus.

This paper introduces FactChecker, language-aware approach to truth-finding. FactChecker differs from prior approaches in that it does not rely on iterative peer voting, instead it leverages language to infer believability of fact candidates. In particular, FactChecker makes use of linguistic features to detect if a given source objectively states facts or is speculative and opinionated. To ensure that fact candidates mentioned in similar sources have similar believability, FactChecker augments objectivity with a co-mention score to compute the overall believability score of a fact candidate. Our experiments on various datasets show that FactChecker yields higher accuracy than existing approaches.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Sociology and Political Science, Natural Language Processing, Information Extraction

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Citations in the corpus

Reasoning Over Semantic-Level Graph for Fact Checking (2019, neutral): “a text or a subject-predicate-object triple (Nakashole and Mitchell, 2014).”
Emergent: a novel data-set for stance classification (2016, background): “more complex than the entity-relation-entity or entity-property-number triples considered in previous work (Nakashole and Mitchell, 2014; Vlachos and Riedel, 2015).”
A Survey on Natural Language Processing for Fake News Detection (2018, methodology): “This approach is used by Nakashole and Mitchell (2014).”
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning (2018, methodology): “Nakashole and Mitchell (2014) proposed a method to extract conﬂicting values from the Web in the form of Subject-Predicate-Object (SPO) triplets and uses language objectivity analysis to determine the true value.”
The Limitations of Stylometry for Detecting Machine-Generated Fake News (2019, background): “…and the increasing interest in fake news, recent studies applied stylometry on entire news articles (Pisarevskaya 2017), short news reports (Pérez-Rosas et al. 2018), fact and political statements (Nakashole and Mitchell 2014; Rashkin et al. 2017) and posts in social media (Volkova et al. 2017).”
Credibility Assessment of Textual Claims on the Web (2016, methodology): “Work in [13] goes a step further by proposing a method to generate conflicting values or fact candidates from Web contents.”
ExFaKT: A Framework for Explaining Facts over Knowledge Graphs and Text (2019, methodology): “• Politicians benchmark [25] with 275 candidates, which contains for each true fact a set of its alternatives.”