Online Spam Review Detection: A Survey of Literature

Li, He; Wang, Xianzhi; Chen, Hongxu; Xu, Guandong

doi:10.1007/s44230-022-00001-3

Cited by 9 publications

(2 citation statements)

References 67 publications

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“…He et al [12] discussed that Type 2 and 3 reviews also attract many researchers' interests because these types of reviews have the potential of fraud. Nevertheless, it was difficult to find relevant studies, except the two early studies [9] and [14] mentioned in Chapter I. Jindal and Liu [9] obtained 98.7% AUC using the logistic regression for total 470 Type 2 and 3 reviews.…”

Section: Detection Of Type 2 and 3 Reviewsmentioning

confidence: 99%

Efficient Detection of Noise Reviews Over a Large Number of Places

Kim,

Park

2023

IEEE Access

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User reviews have been widely used to extract opinions, complaints, and requirements about a given place or product from the users' point of view. In the process of collecting user reviews, a lot of reviews irrelevant to a given search keyword are included in the collection result. Such irrelevant reviews can easily be detected using supervised learning algorithms. However, situation changes when the number of places or products that need to be analyzed increases because manual labeling is required for each collected review. This paper presents a method to detect irrelevant reviews efficiently when a large number of places and reviews need to be analyzed. The basic idea of the proposed method is to expand the target of the learning from an individual place to a group consisting of multiple places. The method can be applied properly to any place whose number of reviews is not enough to perform the training because a classifier obtained by training sufficient reviews included in the places of a group can be used for the places with insufficient reviews in the group. As an initial study of this approach, we tried to check through experiments whether the proposed method can provide higher accuracy than the conventional method where the training is performed for individual places. Our experimental results showed that the average f1-score of the group learning was about 0.931 over real data collected online. For the places with less than 100 reviews, the f1-score of the conventional and proposed methods was 0.651 and 0.865, respectively, showing that 21.4% performance improvement.

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Section: Detection Of Type 2 and 3 Reviewsmentioning

confidence: 99%

Efficient Detection of Noise Reviews Over a Large Number of Places

Kim,

Park

2023

IEEE Access

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“…However, a significant amount of inappropriate content unrelated to the application itself is being disseminated through these online reviews, including descriptions and links related to pornography, gambling, and other activities [4,5]. These posts not only impact users' browsing experience but also require them to spend more time searching for information that is beneficial to them [6]. Therefore, there is an urgent need for efficient, automated content detection methods.…”

Section: Introductionmentioning

confidence: 99%

Detection System Based on Text Adversarial and Multi-Information Fusion for Inappropriate Comments in Mobile Application Reviews

Yu,

Jia,

Hong

2024

Electronics

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With the rapid development of mobile application technology, the content and forms of comments disseminated on the internet are becoming increasingly complex. Various comments serve as users’ firsthand reference materials for understanding the application. However, some comments contain a significant amount of inappropriate content unrelated to the app itself, such as gambling, loans, pornography, and game account recharging, seriously impacting the user experience. Therefore, this article aims to assist users in filtering out irrelevant and inappropriate messages, enabling them to quickly obtain useful and relevant information. This study focuses on analyzing actual comments on various Chinese apps on the Apple App Store. However, these irrelevant comments exhibit a certain degree of concealment, sparsity, and complexity, which increases the difficulty of detection. Additionally, due to language differences, the existing English research methods exhibit relatively poor adaptability to Chinese textual data. To overcome these challenges, this paper proposes a research method named “blend net”, which combines text adversarial and multi-information fusion detection to enhance the overall performance of the system. The experimental results demonstrate that the method proposed in this paper achieves precision and recall rates both exceeding 98%, representing an improvement of at least 2% compared to existing methods.

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RETRACTED ARTICLE: Transparency in healthcare and e-commerce: detecting online fake reviews using a dense neural network model with relevance mapping

Deshai

Rao

2023

Soft Comput

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Online Spam Review Detection: A Survey of Literature

Cited by 9 publications

References 67 publications

Efficient Detection of Noise Reviews Over a Large Number of Places

Efficient Detection of Noise Reviews Over a Large Number of Places

Detection System Based on Text Adversarial and Multi-Information Fusion for Inappropriate Comments in Mobile Application Reviews

RETRACTED ARTICLE: Transparency in healthcare and e-commerce: detecting online fake reviews using a dense neural network model with relevance mapping

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