Use of Transfer Learning for Affordable In-Context Fake Review Generation

Fake content is a noteworthy threat which is managed by assorted means. This is a serious problem for online shopping platforms whose products can be affected by negative or positive reviews. Artificial intelligence is commonly applied for fake review generation, being transfer learning a promising approach to reduce training requirements. However, the feasibility of generating in-context fake reviews using transfer learning has not been explored yet. This paper analyses the suitability of a couple of transformers (T5 and BART) to generate realistic in-context fake reviews. Results show that 1) the diversity of generated reviews is comparable to existing works; 2) human-based detection is close to random; 3) just reviews generated with one of the used transformers can be detected with 38% precision; and 1 h of training and 8 k real reviews are needed to produce realistic fake reviews.