Deceptive Opinion Spam based On Deep Learning

Fahfouh, Anass; Riffi, Jamal; Mahraz, Mohamed Adnane; Yahyaouy, Ali; Tairi, Hamid

doi:10.1109/icds50568.2020.9268772

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…On the other hand, some research explores the utility of Deep Learning techniques. These include Long Short-Term Memory (LSTM) networks , Convolutional Neural Networks (CNN) (Zhao et al, 2018), Bidirectional Long Short-Term Memory (Bi-LSTM) networks (Liu et al, 2020), and Gated Recurrent Units (GRU) (Anass et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Multi-task Ensemble Learning for Fake Reviews Detection and Helpfulness Prediction: A Novel Approach

Melleng,

Loughrey,

2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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Research on fake reviews detection and review helpfulness prediction is prevalent, yet most studies tend to focus solely on either fake reviews detection or review helpfulness prediction, considering them separate research tasks. In contrast to this prevailing pattern, we address both challenges concurrently by employing a multi-task learning approach. We posit that undertaking these tasks simultaneously can enhance the performance of each task through shared information among features. We utilize pre-trained RoBERTa embeddings with a document-level data representation. This is coupled with an array of deep learning and neural network models, including Bi-LSTM, LSTM, GRU, and CNN. Additionally, we employ ensemble learning techniques to integrate these models, with the objective of enhancing overall prediction accuracy and mitigating the risk of overfitting. The findings of this study offer valuable insights to the fields of NLP and machine learning and present a novel perspective on leveraging multi-task learning for the twin challenges of fake reviews detection and review helpfulness prediction.

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Section: Related Workmentioning

confidence: 99%

Multi-task Ensemble Learning for Fake Reviews Detection and Helpfulness Prediction: A Novel Approach

Melleng,

Loughrey,

2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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“…These previous works show deep learning outperforms machine learning in deceptive message identification, where the ultimate cause is that hand-engineered feature extraction (in machine learning techniques) does not provide the necessary semantic information from the text data to discriminate the deceptive indicators [14]. Therefore, we apply deep learning models to detect deceptive Thai-language messages from Facebook sources.…”

Section: Introductionmentioning

confidence: 98%

“…In addition, a multi-instance learning and hierarchical architecture handling variable length review texts were reported to have outperformed other machine learning methods. Anass et al [14] reported the comparison between different neural network architectures and their effectiveness in the detection of deceptive opinion spam. Their results showed that convolutional neural network (CNN) performed better than recurrent neural network (RNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent units (GRU), and bidirectional gated recurrent units (BiGRU).…”

Section: Introductionmentioning

confidence: 99%

Deep learning for classifying thai deceptive messages

Songram

Khummanee

Chomphuwiset

et al. 2023

IJEECS

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Online deception has become a major problem affecting people, society, the economy, and national security. It is mostly done by spreading deceptive messages because message are quickly spread on social networks and are easily accessed by anyone. Detecting deceptive messages is challenging as the messages are unstructured, informal, and complex; this extends into Thai language messages. In this paper, various deep learning models are proposed to detect deceptive messages under two feature extraction trials. A balanced two-class dataset of deceptive and truthful Thai messages (n=2378) is collected from Facebook pages. Instance features are encoded using word embeddings (Thai2Fit) and one-hot encoding techniques. Five classification models, convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent units (BiGRU), CNN-BiLSTM, and CNN-BiGRU, are proposed and evaluated upon the dataset with each feature extraction technique. The experimental results show that all the proposed models had excellent accuracy (95.59% to 98.74%) and BiLSTM with one-hot encoding gave the best performance, achieving 98.74% accuracy.

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Intelligent fake reviews detection based on aspect extraction and analysis using deep learning

Bathla

Singh

et al. 2022

Neural Comput & Applic

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Deceptive Opinion Spam based On Deep Learning

Cited by 7 publications

References 14 publications

Multi-task Ensemble Learning for Fake Reviews Detection and Helpfulness Prediction: A Novel Approach

Multi-task Ensemble Learning for Fake Reviews Detection and Helpfulness Prediction: A Novel Approach

Deep learning for classifying thai deceptive messages

Intelligent fake reviews detection based on aspect extraction and analysis using deep learning

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