Constructing and evaluating a novel crowdsourcing-based paraphrased opinion spam dataset

Seongsoon Kim, Seongwoon Lee, Donghyeon Park, Jaewoo Kang

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

3 Citations (Scopus)

Abstract

Opinion spam, intentionally written by spammers who do not have actual experience with services or products, has recently become a factor that undermines the credibility of information online. In recent years, studies have attempted to detect opinion spam using machine learning algorithms. However, limitations of gold-standard spam datasets still prove to be a major obstacle in opinion spam research. In this paper, we introduce a novel dataset called Paraphrased OPinion Spam (POPS), which contains a new type of review spam that imitates real human opinions using crowdsourcing. To create such a seemingly truthful review spam dataset, we asked task participants to paraphrase truthful reviews, and include factual information and domain knowledge in their reviews. The classification experiments and semantic analysis results show that our POPS dataset most linguistically and semantically resembles truthful reviews. We believe that our new deceptive opinion spam dataset1 will help advance opinion spam research.

Original languageEnglish
Title of host publication26th International World Wide Web Conference, WWW 2017
PublisherInternational World Wide Web Conferences Steering Committee
Pages827-836
Number of pages10
ISBN (Print)9781450349147
DOIs
Publication statusPublished - 2017 Jan 1
Event26th International World Wide Web Conference, WWW 2017 - Perth, Australia
Duration: 2017 Apr 32017 Apr 7

Other

Other26th International World Wide Web Conference, WWW 2017
CountryAustralia
CityPerth
Period17/4/317/4/7

Keywords

  • Crowdsourcing
  • Deceptive opinion spam
  • Paraphrased opinion spam

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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  • Cite this

    Kim, S., Lee, S., Park, D., & Kang, J. (2017). Constructing and evaluating a novel crowdsourcing-based paraphrased opinion spam dataset. In 26th International World Wide Web Conference, WWW 2017 (pp. 827-836). [3052607] International World Wide Web Conferences Steering Committee. https://doi.org/10.1145/3038912.3052607