Fact or Factitious? Contextualized Opinion Spam Detection

• URL: http://arxiv.org/abs/2010.15296v1
• Date: Thu, 29 Oct 2020 00:59:06 GMT
• Title: Fact or Factitious? Contextualized Opinion Spam Detection
• Authors: Stefan Kennedy and Niall Walsh, Kirils Sloka, Jennifer Foster, Andrew McCarren
• Abstract summary: We apply a number machine learning approaches found to be effective, and introduce our own approach by fine-tuning state of the art contextualised embeddings. The results we obtain show the potential of contextualised embeddings for fake review detection, and lay the groundwork for future research in this area.
• Score: 9.415901312074336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we perform an analytic comparison of a number of techniques used to detect fake and deceptive online reviews. We apply a number machine learning approaches found to be effective, and introduce our own approach by fine-tuning state of the art contextualised embeddings. The results we obtain show the potential of contextualised embeddings for fake review detection, and lay the groundwork for future research in this area.

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