Devising a Cross-Domain Model to Detect Fake Review Comments

Chen Shan Wei, Ping Yu Hsu, Chen Wan Huang, Ming Shien Cheng, Grandys Frieska Prassida

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The online reviews not only have huge impact on consumer shopping behavior but also online stores’ marketing strategy. Positive reviews will have positive influence for consumer’s buying decision. Therefore, some sellers want to boost their sales volume. They will hire spammers to write undeserving positive reviews to promote their products. Currently, some of the researches related to detection of fake reviews based on the text feature, the model will reach to high accuracy. However, the same model test on the other dataset the accuracy decrease sharply. Relevant researches had gradually explored the identification of fake reviews across different domains, whether the model built using comprehensive methods such as text features or neural networks, encountering the decreasing of accuracy. On the other hand, the method didn’t explain why the model can be applied to cross-domain predictions. In our research, we using the fake reviews and truthful reviews from Ott et al. (2011) and Li, Ott, Cardie, and Hovy (2014) in the three domain (hotel, restaurant, doctor). The cross-domain detect model based on Stimuli Organism Response (S-O-R) combine LIWC (Linguistic Inquiry and Word Count), add word2vec quantization feature, overcoming the decreasing accuracy situation. According to the research result, in the method one SOR calculation of feature weight of reviews, the DNN classification algorithm accuracy is 63.6%. In the method two, calculation of frequent features of word vectors, the random forest classification algorithm accuracy is 73.75%.

Original languageEnglish
Title of host publicationAdvances in Computational Collective Intelligence - 12th International Conference, ICCCI 2020, Proceedings
EditorsMarcin Hernes, Krystian Wojtkiewicz, Edward Szczerbicki
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030631185
StatePublished - 2020
Event12th International Conference on International Conference on Computational Collective Intelligence, ICCCI 2020 - Da Nang, Viet Nam
Duration: 30 Nov 20203 Dec 2020

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference12th International Conference on International Conference on Computational Collective Intelligence, ICCCI 2020
Country/TerritoryViet Nam
CityDa Nang


  • Fake reviews
  • Stimuli-Organism-Response (S-O-R) framework
  • word2vec


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