Online “helpful” Lies: An Empirical Study of Helpfulness in Fake and Authentic Online Reviews

Chua, Alton Y. K.; Chen, Xiaoyu

doi:10.1007/978-3-030-96957-8_10

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…Snowball is an improved version of the Porter algorithm and can handle multiple languages, while Lovins uses substitution rules for English text but has some limitations. Dawson is a more complex algorithm that considers the context and part of speech of a word and is effective for non-English languages (Chua and Chen 2022 ). Porter is widely used and easy to implement, but it may over-stem or under-stem certain words.…”

Section: Methodsmentioning

confidence: 99%

Unmasking deception: a CNN and adaptive PSO approach to detecting fake online reviews

Deshai

Rao

2023

Soft Comput

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Online reviews play a critical role in modern word-of-mouth communication, influencing consumers' shopping preferences and purchase decisions, and directly affecting a company's reputation and profitability. However, the credibility and authenticity of these reviews are often questioned due to the prevalence of fake online reviews that can mislead customers and harm e-commerce's credibility. These fake reviews are often difficult to identify and can lead to erroneous conclusions in user feedback analysis. This paper proposes a new approach to detect fake online reviews by combining convolutional neural network (CNN) and adaptive particle swarm optimization with natural language processing techniques. The approach uses datasets from popular online review platforms like Ott, Amazon, Yelp, TripAdvisor, and IMDb and applies feature selection techniques to select the most informative features. The paper suggests using attention mechanisms like bidirectional encoder representations from transformers and generative pre-trained transformer, as well as other techniques like Deep contextualized word representation, word2vec, GloVe, and fast Text, for feature extraction from online review datasets. The proposed method uses a multimodal approach based on a CNN architecture that combines text data to achieve a high accuracy rate of 99.4%. This outperforms traditional machine learning classifiers in terms of accuracy, recall, and F measure. The proposed approach has practical implications for consumers, manufacturers, and sellers in making informed product choices and decision-making processes, helping maintain the credibility of online consumer reviews. The proposed model shows excellent generalization abilities and outperforms conventional discrete and existing neural network benchmark models across multiple datasets. Moreover, it reduces the time complexity for both training and testing.

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Section: Methodsmentioning

confidence: 99%

Unmasking deception: a CNN and adaptive PSO approach to detecting fake online reviews

Deshai

Rao

2023

Soft Comput

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“…Zeng et al [34] examined the impact of sentiment features and product type on review helpfulness predication, and the results revealed that sentiment features are more useful than the product type. Chua and Chen [35] used empirical studies to explore the relationship between useful and false reviews, and the results suggested that sentiment features can help to distinguish helpful false reviews from helpful authentic reviews.…”

Section: Identification Of Helpful Onlinementioning

confidence: 99%

“…Positive words Cao et al [30]; Chua and Banerjee [31]; Zeenia Singla and Sushma [28]; Liu et al [32]; Oumayma Oueslati and Sushma [33]; Zeng et al [34]; Chua and Chen [35] Negative words Cao et al [30]; Chua and Banerjee [31]; Zeenia Singla and Sushma [28]; Liu et al [32]; Oumayma Oueslati and Sushma [33]; Zeng et al [34] [38]; Krishnamoorthy [39]; Jiang et al [18]; Du et al [40]; Shan et al [41] segmentation in green hotels. LDA was used to extract different aspects of customer satisfaction.…”

Section: Sentimentmentioning

confidence: 99%

Discovery of Product Features for Redesign from User Implicit Feedback

Wang

et al. 2022

Mobile Information Systems

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Online reviews are key sources to help manufacturers design and improve their products. However, using online reviews for product redesign is challenging because many online reviews contain useless information, and customers rarely provide explicit information about the products. The few methods available to recognize the customers’ demands from online reviews have several limitations, including failing to consider the redundancy of useless reviews, confusion in providing targeted information among different aspects of product design, and disregarding the manufacturing limitation. Hence, considering customer needs and manufacturing constraints for product improvement, this study proposes a new model called DPFR to discover the potential product features for redesign. In DPFR, it first analyzes and predicts helpfulness of reviews for more effective decision of product design and improvement, which is neglected in previous research. Then, a structured model based on topic modeling is conducted. Overlooked by previous researchers, DPFR also introduces a feature importance matric to measure the significance of each commented feature to product redesign. Third, a target features selection model is conducted further considering the limitations of manufacturers rather than only focusing on customer satisfaction. The effectiveness of the proposed model was assessed using a case study of data acquired from Bilibili, a large short-video platform in China. Focusing only on customer satisfaction, the results confirm the effectiveness of DPFR. Finally, including the constraints of product redesign, DPFR balances customer satisfaction and the manufacturing constraints such as redesign cost, redesign time, and carbon emissions. This study provides more practical suggestions for manufacturers to discover product redesign features that result in strategic product improvement decisions.

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Online “helpful” Lies: An Empirical Study of Helpfulness in Fake and Authentic Online Reviews

Cited by 2 publications

References 12 publications

Unmasking deception: a CNN and adaptive PSO approach to detecting fake online reviews

Unmasking deception: a CNN and adaptive PSO approach to detecting fake online reviews

Discovery of Product Features for Redesign from User Implicit Feedback

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