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A Deep Hybrid Model for fake review detection by jointly leveraging review text, overall ratings, and aspect ratings

  • Data analytics and machine learning
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Abstract

Recently, product/ service reviews and online businesses have been similar to the blood–heart relationship as they greatly impact customers’ purchase decisions. There is an increasing incentive to manipulate reviews, mostly profit-motivated, as positive reviews imply high purchases and vice versa. Therefore, a suitable fake review detection approach is paramount in ensuring fair e-business competition and sustainability. Most existing methods mainly utilize discrete review features such as text similarity, rating deviation, review content, product information, the semantic meaning of reviews, and reviewer behaviors. In the matter of discourse, some recent researchers attempted multi-feature (review- and reviewer-centric features) integration. However, such approaches face two issues: (1) Review representation is extracted in an independent manner, thus ignoring correlations between them (2) Lack of a unified framework that can jointly learn latent text feature vectors, aspect ratings, and overall rating. To address the named issues, we propose a novel Deep Hybrid Model for fake review detection, which jointly learns from latent text feature vectors, aspect ratings, and overall ratings. Initially, it computes contextualized review text vectors, extracts aspects, and calculates respective rating values. Then, contextualized word vectors, overall ratings, and aspect ratings are concatenated. Finally, the model learns to classify reviews from such unified multi-dimensional feature representation. Extensive experiments on a publicly available dataset demonstrate that the proposed approach significantly outperforms state-of-the-art baseline approaches.

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Data availability

Enquiries about data availability should be directed to the authors.

Notes

  1. https://code.google.com/archive/p/stop-words/

  2. http://odds.cs.stonybrook.edu/yelpchi-dataset/

Abbreviations

ACB:

Attention-based CNN Bi-LSTM

AMT:

Amazon mechanical turk

AR-LOF:

Aspects rating local outlier factor

BERT:

Bidirectional encoder representations from transformers

Bi-LSTM:

Bidirectional Long Short-Term Memory

bfGAN:

Behavioral feature Generative Adversarial network

CNN:

Convolution neural network

DFFNN:

Deep feed-forward neural network

DL:

Deep learning

DSRHA:

Detection of spam reviews through a hierarchical attention architecture with N-gram CNN and Bi-LSTM

FABC:

Fuzzy artificial bee colony

\(F_N\) :

False negative

\(F_P\) :

False positive

IP:

Internet protocol

LARA:

Latent aspects rating analysis LDA Latent Dirichlet Allocation

LSTM:

Long short-term memory

MAC:

Media access control

ML:

Machine learning

MLP:

Multilayer perceptron

NB:

Naïve Bayes

NLTK:

Natural language toolkit

NN:

Neural network

NRC:

National research council canada

ReLU:

Rectified linear unit

RF:

Random forest

SVM:

Support vector machines

\(T_N\) :

True negative

\(T_P\) :

True positive

VPN:

Virtual private network

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Funding

This work was supported by the National Key R &D Program of China under Grants 2019YFB1406302, and the National Natural Science Foundation of China under Grant 62272048.

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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, People’s Republic of China

    Ramadhani Ally Duma, Zhendong Niu, Jude Tchaye-Kondi & Abdulganiyu Abdu Yusuf

  2. College of Informatics and Virtual Education, The University of Dodoma, Dodoma, Tanzania

    Ramadhani Ally Duma & Ally S. Nyamawe

  3. School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, USA

    Zhendong Niu

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  1. Ramadhani Ally Duma
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  2. Zhendong Niu
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Contributions

RAD Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing—original draft, visualization. ZN Methodology, Resources, Supervision, Validation, Visualization, Writing—review & editing, Project administration. AN Conceptualization, Methodology, Writing—review & editing JT-K Software, Methodology, Data curation AY Writing—review & editing

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Correspondence to Zhendong Niu.

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Duma, R.A., Niu, Z., Nyamawe, A.S. et al. A Deep Hybrid Model for fake review detection by jointly leveraging review text, overall ratings, and aspect ratings. Soft Comput 27, 6281–6296 (2023). https://doi.org/10.1007/s00500-023-07897-4

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