Fact or Factitious? Contextualized Opinion Spam Detection
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 0; 2025: 2.
6 papers.
- 2025OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviewscited by 3
- 2021Deception detection in text and its relation to the cultural dimension of individualism/collectivismcited by 2
- 2022Explainable Verbal Deception Detection using Transformerscited by 2
- 2023Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networkscited by 2
- 2025Context-Aware Pragmatic Metacognitive Prompting for Sarcasm Detectioncited by 2
- 2022Signed Latent Factors for Spamming Activity Detectioncited by 0
- 2025Context-Aware Pragmatic Metacognitive Prompting for Sarcasm Detection26 Nov 2025
- 2025OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviews30 Aug 2025
- 2023Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks5 Apr 2023
- 2022Explainable Verbal Deception Detection using Transformers6 Oct 2022
- 2022Signed Latent Factors for Spamming Activity Detection28 Sep 2022
- 2021Deception detection in text and its relation to the cultural dimension of individualism/collectivism26 May 2021