@inproceedings{092d65638527482ea89a5773beac8815,
title = "Linguistic Features for Detecting Fake Reviews",
abstract = "Online reviews play an integral part for success or failure of businesses. Prior to purchasing services or goods, customers first review the online comments submitted by previous customers. However, it is possible to superficially boost or hinder some businesses through posting counterfeit and fake reviews. This paper explores a natural language processing approach to identify fake reviews. We present a detailed analysis of linguistic features for distinguishing fake and trustworthy online reviews. We study 15 linguistic features and measure their significance and importance towards the classification schemes employed in this study. Our results indicate that fake reviews tend to include more redundant terms and pauses, and generally contain longer sentences. The application of several machine learning classification algorithms revealed that we were able to discriminate fake from real reviews with high accuracy using these linguistic features.",
keywords = "deception detection, fake review, linguistic features, machine learning",
author = "Faranak Abri and Gutierrez, {Luis Felipe} and Namin, {Akbar Siami} and Jones, {Keith S.} and Sears, {David R.W.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 ; Conference date: 14-12-2020 Through 17-12-2020",
year = "2020",
month = dec,
doi = "10.1109/ICMLA51294.2020.00063",
language = "English",
series = "Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "352--359",
editor = "Wani, {M. Arif} and Feng Luo and Xiaolin Li and Dejing Dou and Francesco Bonchi",
booktitle = "Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020",
}