Abstract
The issue of fake reviews on online platforms has increasingly become a huge concern in recent years, with the potential to mislead consumers and negatively impact businesses. In this paper, we present a comprehensive approach to detecting fake reviews using both supervised and unsupervised learning techniques. Our approach includes classic machine learning algorithms, deep learning techniques such as RNN and attention networks, as well as state-of-the-art models like BERT and GPT. We leverage a labeled dataset of restaurant reviews from Yelp.com to train and evaluate our models. We also compare the performance of supervised and unsupervised learning techniques and identify the most effective and explainable models for detecting fake reviews. Our results show that our approach achieves high accuracy in detecting fake reviews, and the interpretation of our models offers valuable insights into the factors that contribute to the identification of fake reviews. We believe our work contributes to the ongoing effort of combating fake reviews and provides a practical and effective solution for businesses and consumers to identify trustworthy reviews.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alameri SA, Mohd M (2021) Comparison of fake news detection using machine learning and deep learning techniques. In: 2021 3rd international cyber resilience conference (CRC), Langkawi Island, Malaysia, pp 1–6. https://doi.org/10.1109/CRC50527.2021.9392458
Fake review detection on yelp dataset using classification techniques in machine learning. IEEE (2019)
Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding
Manzoor SI, Singla J, Nikita (2019) Fake news detection using machine learning approaches: a systematic review. In: 2019 3rd international conference on trends in electronics and informatics (ICOEI), Tirunelveli, India, pp 230–234. https://doi.org/10.1109/ICOEI.2019.8862770
Hájek P et al (2020) Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining. Neural Comput Appl 1–16
Elmogy AM, Tariq U, Mohammed A, Ibrahim A (2021) Fake reviews detection using supervised machine learning. Int J Adv Comput Sci Appl (IJACSA) 12(1)
Baishya D, Deka JJ, Dey G et al (2021) SAFER: sentiment analysis-based fake review detection in E-commerce using deep learning. SN Comput Sci 2:479
Gutiérrez-Espinoza L et al (2020) Fake reviews detection through ensemble learning. arXiv abs/2006.07912
Mridha MF, Keya AJ, Hamid MA, Monowar MM, Rahman MS (2021) A comprehensive review on fake news detection with deep learning. IEEE Access 9:156151–156170. https://doi.org/10.1109/ACCESS.2021.3129329
Shahariar GM, Biswas S, Omar F, Shah FM, Binte Hassan S (2019) Spam review detection using deep learning. In: 2019 IEEE 10th annual information technology, electronics and mobile communication conference (IEMCON), Vancouver, BC, Canada, pp 0027–0033. https://doi.org/10.1109/IEMCON.2019.8936148
Sadiq S, Umer M, Ullah S, Mirjalili S, Rupapara V, Nappi M (2021) Discrepancy detection between actual user reviews and numeric ratings of Google App store using deep learning. Expert Syst Appl 181:115111. ISSN 0957-4174
Rodrigues JC, Rodrigues JT, Gonsalves VLK, Naik AU, Shetgaonkar P, Aswale S (2020) Machine & deep learning techniques for detection of fake reviews: a survey. In: 2020 international conference on emerging trends in information technology and engineering (ic-ETITE), Vellore, India, pp 1–8. https://doi.org/10.1109/ic-ETITE47903.2020.063
Zichao et al (2016) Hierarchical attention networks for document classification
Javed MS, Majeed H, Mujtaba H et al (2021) Fake reviews classification using deep learning ensemble of shallow convolutions. J Comput Soc Sci 4:883–902
Jian Y, Chen X, Wang H (2022) Fake restaurant review detection using deep neural networks with hybrid feature fusion method. In: Database systems for advanced applications. DASFAA 2022. Lecture notes in computer science, vol 13247. Springer, Cham
Barushka A, Hajek P (2019) Review spam detection using word embeddings and deep neural networks. In: MacIntyre J, Maglogiannis I, Iliadis L, Pimenidis E (eds) Artificial intelligence applications and innovations. AIAI 2019. IFIP advances in information and communication technology, vol 559. Springer, Cham
Arjun et al (2013) What yelp fake review filter might be doing? In: ICWSM
Jianping et al (2020) Bidirectional LSTM with hierarchical attention for text classification
GPT2 Hugging face. https://huggingface.co/gpt2
Bhat MQ, Alex SA, Nanda S, Goutham S (2022) Qualitative analysis of anomaly detection in time series. In: 2022 4th international conference on circuits, control, communication and computing (I4C), Bangalore, India, pp 250–253. https://doi.org/10.1109/I4C57141.2022.10057732
Daochen Z, Bhat ZP, Chen Y-W, Wang Y, Ding S, Chen J, Lai K-H et al (2021) AutoVideo: an automated video action recognition system. arXiv preprint
Visualization. https://github.com/jessevig/bertviz
Acknowledgements
We would like to extend our appreciation to all those who played a crucial role in the completion of this paper. We are immensely grateful to the management and Dr. N. V. R. Naidu, the principal of M.S.R.I.T., Bengaluru, for providing us with the opportunity to explore our potential. We would also like to express our deep gratitude to Dr. Annapurna P. Patil, the Head of the Department of CSE, for her constant support and guidance. Our sincere thanks goes to our paper’s advisor, Dr. Jayalakshmi D. S., for instilling in us the determination and resilience to overcome every obstacle encountered on this journey and for continually motivating us to reach our fullest potential.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bhat, M.Q., Jayalakshmi, D.S., Mallegowda, M., Geetha, J. (2024). Interpreting Fake Reviews Using Machine Learning and Deep Learning. In: Iglesias, A., Shin, J., Patel, B., Joshi, A. (eds) Proceedings of World Conference on Information Systems for Business Management. ISBM 2023. Lecture Notes in Networks and Systems, vol 833. Springer, Singapore. https://doi.org/10.1007/978-981-99-8346-9_24
Download citation
DOI: https://doi.org/10.1007/978-981-99-8346-9_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8345-2
Online ISBN: 978-981-99-8346-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)