Towards a General Rule for Identifying Deceptive Opinion Spam

By Jiwei Li, Myle Ott, Claire Cardie, Eduard H. Hovy. Annual Meeting of the Association for Computational Linguistics, 2014

380 citations recorded by the source corpus.

Consumers’ purchase decisions are increasingly influenced by user-generated online reviews. Accordingly, there has been growing concern about the potential for posting deceptive opinion spam— fictitious reviews that have been deliberately written to sound authentic, to deceive the reader. In this paper, we explore generalized approaches for identifying online deceptive opinion spam based on a new gold standard dataset, which is comprised of data from three different domains (i.e. Hotel, Restaurant, Doctor), each of which contains three types of reviews, i.e. customer generated truthful reviews, Turker generated deceptive reviews and employee (domain-expert) generated deceptive reviews. Our approach tries to capture the general difference of language usage between deceptive and truthful reviews, which we hope will help customers when making purchase decisions and review portal operators, such as TripAdvisor or Yelp, investigate possible fraudulent activity on their sites. 1

Subfields: Information Systems, Social Psychology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/2b3e4df9463e721577f7058d39949b0d74fd58a9

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Citations in the corpus

Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, background): “2004b], reviews [Li et al. 2014; Mukherjee et al. 2013b; Ott et al. 2011; Popoola 2018; Shojaee et al. 2013; Zhang et al. 2016], statements [Fuller et al.”
False Information on Web and Social Media: A Survey (2018, background): “[54] collected additional deceptive reviews from domain experts such as employees at target venues.”
Description Based Text Classification with Reinforcement Learning (2020, background): “, 2012; Wang & Manning, 2012), spam detection (Ott et al., 2011; 2013; Li et al., 2014), etc.”
Deception Detection using Real-life Trial Data (2015, background): “To date, several research publications on verbal-based deception detection have explored the identiﬁcation of deceptive content in a variety of domains, including online dating websites [39, 18], forums [42, 23], social networks [21], and consumer report web-sites [31, 24].”
Automated Crowdturfing Attacks and Defenses in Online Review Systems (2017, background): “Furthermore, researchers have extensively studied detection of opinion spam or fake reviews in online review systems using features based on review content [32, 48, 53] and a variety of metadata [10, 19, 20].”
Authorship Attribution for Neural Text Generation (2020, methodology): “Overall, the patterns among these LIWC features follow our observations that GPT2, GROVER and FAIR generally have higher news generation quality than other machine algorithms.”