TruthCore: Non-parametric estimation of truth from a collection of authoritative sources

Mukherjee, Tathagata; Parajuli, Biswas; Kumar, Piyush; Pasiliao, Eduardo L.

doi:10.1109/bigdata.2016.7840696

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Truth Discovery: Truth discovery has attracted much attention recently. Different approaches have been proposed to address different scenarios [13,20,29]. Most of the truth discovery approaches are tailored to categorical data and thus assume there is a single objective truth that can be derived from the claims of different sources [15].…”

Section: Related Workmentioning

confidence: 99%

CrowdQM: Learning Aspect-Level User Reliability and Comment Trustworthiness in Discussion Forums

Morales

Narang

Sundaram

et al. 2020

Advances in Knowledge Discovery and Data Mining

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Community discussion forums are increasingly used to seek advice; however, they often contain conflicting and unreliable information. Truth discovery models estimate source reliability and infer information trustworthiness simultaneously in a mutual reinforcement manner, and can be used to distinguish trustworthy comments with no supervision. However, they do not capture the diversity of word expressions and learn a single reliability score for the user. CrowdQM addresses these limitations by modeling the fine-grained aspect-level reliability of users and incorporate semantic similarity between words to learn a latent trustworthy comment embedding. We apply our latent trustworthy comment for comment ranking for three diverse communities in Reddit and show consistent improvement over non-aspect based approaches. We also show qualitative results on learned reliability scores and word embeddings by our model.A. Morales and K. Narang-Equal Contribution.

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Section: Related Workmentioning

confidence: 99%