# Fact or Factitious? Contextualized Opinion Spam Detection

arXiv: https://arxiv.org/abs/2010.15296 · HTML: https://mlsanity.com/paper/2010.15296/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviews](https://mlsanity.com/paper/2509.00285/) (2025)
- [Deception detection in text and its relation to the cultural dimension of individualism/collectivism](https://mlsanity.com/paper/2105.12530/) (2021)
- [Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks](https://mlsanity.com/paper/2304.02739/) (2023)
- [Explainable Verbal Deception Detection using Transformers](https://mlsanity.com/paper/2210.03080/) (2022)
- [Context-Aware Pragmatic Metacognitive Prompting for Sarcasm Detection](https://mlsanity.com/paper/2511.21066/) (2025)
- [Signed Latent Factors for Spamming Activity Detection](https://mlsanity.com/paper/2209.13814/) (2022)

## 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.