Do not trust the trolls: Predicting credibility in community question answering forums

Preslav Nakov, Tsvetomila Mihaylova, Lluis Marques, Yashkumar Shiroya, Ivan Koychev

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

8 Citations (Scopus)

Abstract

We address information credibility in community forums, in a setting in which the credibility of an answer posted in a question thread by a particular user has to be predicted. First, we motivate the problem and we create a publicly available annotated English corpus by crowd-sourcing. Second, we propose a large set of features to predict the credibility of the answers. The features model the user, the answer, the question, the thread as a whole, and the interaction between them. Our experiments with ranking SVMs show that the credibility labels can be predicted with high performance according to several standard IR ranking metrics, thus supporting the potential usage of this layer of credibility information in practical applications. The features modeling the profile of the user (in particular trollness) turn out to be most important, but embedding features modeling the answer and the similarity between the question and the answer are also very relevant. Overall, half of the gap between the baseline performance and the perfect classifier can be covered using the proposed features.

Original languageEnglish
Title of host publicationInternational Conference on Recent Advances in Natural Language Processing
Subtitle of host publicationMeet Deep Learning, RANLP 2017 - Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages551-560
Number of pages10
Volume2017-September
ISBN (Electronic)9789544520489
DOIs
Publication statusPublished - 1 Jan 2017
Event11th International Conference on Recent Advances in Natural Language Processing, RANLP 2017 - Varna, Bulgaria
Duration: 2 Sep 20178 Sep 2017

Other

Other11th International Conference on Recent Advances in Natural Language Processing, RANLP 2017
CountryBulgaria
CityVarna
Period2/9/178/9/17

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Electrical and Electronic Engineering

Cite this

Nakov, P., Mihaylova, T., Marques, L., Shiroya, Y., & Koychev, I. (2017). Do not trust the trolls: Predicting credibility in community question answering forums. In International Conference on Recent Advances in Natural Language Processing: Meet Deep Learning, RANLP 2017 - Proceedings (Vol. 2017-September, pp. 551-560). Association for Computational Linguistics (ACL). https://doi.org/10.26615/978-954-452-049-6-072