Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection

By David Ifeoluwa Adelani, H. Mai, Fuming Fang, Huy Hoang Le Nguyen, Junichi Yamagishi, Isao Echizen. 2019

148 citations recorded by the source corpus.

Advanced neural language models (NLMs) are widely used in sequence generation tasks because they are able to produce fluent and meaningful sentences. They can also be used to generate fake reviews, which can then be used to attack online review systems and influence the buying decisions of online shoppers. To perform such attacks, it is necessary for experts to train a tailored LM for a specific topic. In this work, we show that a low-skilled threat model can be built just by combining publicly available LMs and show that the produced fake reviews can fool both humans and machines. In particular, we use the GPT-2 NLM to generate a large number of high-quality reviews based on a review with the desired sentiment and then using a BERT based text classifier (with accuracy of 96%) to filter out reviews with undesired sentiments. Because none of the words in the review are modified, fluent samples like the training data can be generated from the learned distribution. A subjective evaluation with 80 participants demonstrated that this simple method can produce reviews that are as fluent as those written by people. It also showed that the participants tended to distinguish fake reviews randomly. Three countermeasures, Grover, GLTR, and OpenAI GPT-2 detector, were found to be difficult to accurately detect fake review.

Subfields: Artificial Intelligence, Natural Language Processing, Generative Models

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Citations in the corpus

Automatic Detection of Generated Text is Easiest when Humans are Fooled (2019, background): “State-of-the-art generative language models are now capable of producing multi-paragraph excerpts that at a surface level are virtually indistinguishable from human-written content (Zellers et al., 2019; Radford et al., 2019; Adelani et al., 2020).”
Can AI-Generated Text be Reliably Detected? (2023, background): “AI tools have the potential to be misused by users for unethical purposes such as plagiarism, generating fake news, spamming, generating fake product reviews, and manipulating web content for social engineering in ways that can have negative impacts on society [11, 12].”
Automatic Detection of Machine Generated Text: A Critical Survey (2020, methodology): “, 2020), fake product reviews (Adelani et al., 2020), and fake comments (Weiss, 2019) generated by TGM only at chance level.”
Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature (2023, background): “However, their misuse also introduces concerns—especially regarding fake news (Ahmed et al., 2021), malicious product reviews (Adelani et al., 2020), and plagiarism (Lee et al., 2023).”