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

Alex Morales, Kanika Narang, Hari Sundaram, Chengxiang Zhai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings
EditorsHady W. Lauw, Ee-Peng Lim, Raymond Chi-Wing Wong, Alexandros Ntoulas, See-Kiong Ng, Sinno Jialin Pan
PublisherSpringer
Pages592-605
Number of pages14
ISBN (Print)9783030474256
DOIs
StatePublished - 2020
Event24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 - Singapore, Singapore
Duration: May 11 2020May 14 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12084 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020
CountrySingapore
CitySingapore
Period5/11/205/14/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'CrowdQM: Learning Aspect-Level User Reliability and Comment Trustworthiness in Discussion Forums'. Together they form a unique fingerprint.

  • Cite this

    Morales, A., Narang, K., Sundaram, H., & Zhai, C. (2020). CrowdQM: Learning Aspect-Level User Reliability and Comment Trustworthiness in Discussion Forums. In H. W. Lauw, E-P. Lim, R. C-W. Wong, A. Ntoulas, S-K. Ng, & S. J. Pan (Eds.), Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings (pp. 592-605). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12084 LNAI). Springer. https://doi.org/10.1007/978-3-030-47426-3_46