Truth Discovery by Claim and Source Embedding

Lyu, Shanshan; Ouyang, Wentao; Shen, Huawei; Cheng, Xueqi

doi:10.1145/3132847.3133069

Cited by 8 publications

(1 citation statement)

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“…Our work is closely related to the following areas: Truth discovery: Prior approaches for truth discovery (Yin et al, 2008;Dong et al, 2009Dong et al, , 2015Li et al, 2011Li et al, , 2014Roth, 2011, 2013;Ma et al, 2015;Lyu et al, 2017) have focused on structured data with the goal of addressing the problem of conflict resolution amongst multisource data. 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.…”

Section: Related Workmentioning

confidence: 99%

DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning

Popat

Mukherjee²,

Yates

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

196

191

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Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-toend model for evidence-aware credibility assessment of arbitrary textual claims, without any human intervention. It presents a neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources. It also derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Experiments with four datasets and ablation studies show the strength of our method. 1 As fully objective and unarguable truth is often elusive or ill-defined, we use the term credibility rather than "truth".

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