Detecting singleton spams in reviews via learning deep anomalous temporal aspect-sentiment patterns

Shaalan, Yassien; Zhang, Xiuzhen; Chan, Jeffrey; Salehi, Mahsa

doi:10.1007/s10618-020-00725-5

Cited by 9 publications

(6 citation statements)

References 74 publications

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“…Overall, the proposed model has an accuracy of 0.858%. Spam reviewer detection was the focus of another paper [ 26 ], in which an unsupervised sentiment model based on Boltzmann machines was used to distinguish legitimate reviewers from spammers by supplying more text but using less relevant characteristics of an entity. This system was also trained to watch the progression of ideas over time, as spammers tend to focus for short periods of time to distort public opinion the most.…”

Section: Background Of Studymentioning

confidence: 99%

Online Troll Reviewer Detection Using Deep Learning Techniques

Al-Adhaileh

Aldhyani

Alghamdi

2022

Applied Bionics and Biomechanics

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The concentration of this paper is on detecting trolls among reviewers and users in online discussions and link distribution on social news aggregators such as Reddit. Trolls, a subset of suspicious reviewers, have been the focus of our attention. A troll reviewer is distinguished from an ordinary reviewer by the use of sentiment analysis and deep learning techniques to identify the sentiment of their troll posts. Machine learning and lexicon-based approaches can also be used for sentiment analysis. The novelty of the proposed system is that it applies a convolutional neural network integrated with a bidirectional long short-term memory (CNN–BiLSTM) model to detect troll reviewers in online discussions using a standard troll online reviewer dataset collected from the Reddit social media platform. Two experiments were carried out in our work: the first one was based on text data (sentiment analysis), and the second one was based on numerical data (10 attributes) extracted from the dataset. The CNN-BiLSTM model achieved 97% accuracy using text data and 100% accuracy using numerical data. While analyzing the results of our model, we observed that it provided better results than the compared methods.

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Section: Background Of Studymentioning

confidence: 99%

Online Troll Reviewer Detection Using Deep Learning Techniques

Al-Adhaileh

Aldhyani

Alghamdi

2022

Applied Bionics and Biomechanics

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“…The work [ 15 ] tried to solve the problem of spam reviewer detection. Authors at first learned an unsupervised deep aspect level sentiment model using Boltzman machines to discriminate genuine reviewers, which usually focus on important aspects of entities, from spam reviewers providing more text but on less important aspects.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data

Machová

Mach

Vasilko

2021

Sensors

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The article focuses on solving an important problem of detecting suspicious reviewers in online discussions on social networks. We have concentrated on a special type of suspicious authors, on trolls. We have used methods of machine learning for generation of detection models to discriminate a troll reviewer from a common reviewer, but also methods of sentiment analysis to recognize the sentiment typical for troll’s comments. The sentiment analysis can be provided also using machine learning or lexicon-based approach. We have used lexicon-based sentiment analysis for its better ability to detect a dictionary typical for troll authors. We have achieved Accuracy = 0.95 and F1 = 0.80 using sentiment analysis. The best results using machine learning methods were achieved by support vector machine, Accuracy = 0.986 and F1 = 0.988, using a dataset with the set of all selected attributes. We can conclude that detection model based on machine learning is more successful than lexicon-based sentiment analysis, but the difference in accuracy is not so large as in F1 measure.

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“…ey believed those reviewers who have posted a large number of reviews that do not meet the average rating are considered to be spammers with a high probability. In addition, Shaalan et al [21] conducted detection on singleton reviews, which is one time reviews. ey observed that genuine opinions are usually directed uniformly toward important aspects of entities, while spammers attempt to counter the consensus toward these aspects to cover their malicious intent by adding more text but on less important aspects.…”

Section: Unsupervised Learning Techniquesmentioning

confidence: 99%

Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning

Zhong

Liu

et al. 2021

Complexity

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With the rapid growth of online product reviews, many users refer to others’ opinions before deciding to purchase any product. However, unfortunately, this fact has promoted the constant use of fake reviews, resulting in many wrong purchase decisions. The effective identification of deceptive reviews becomes a crucial yet challenging task in this research field. The existing supervised learning methods require a large number of labeled examples of deceptive and truthful opinions by domain experts, while the available unsupervised learning methods are inefficient because they depend on the features of reviewers to detect each fake review. Therefore, by focusing on the detection efficiency problem and the limitation of large amount of labeled examples dependence, in this paper, we proposed an effective semisupervised learning approach for detecting spam reviews. Firstly, a time series model of all the reviews of a product is constructed, and then the suspected time intervals are captured based on the burst review increases in these intervals. Secondly, a co-training two-view semisupervised learning algorithm was performed in each captured interval, in which linguistic cues, metadata, and user purchase behaviors were synthetically employed to classify the reviews and check whether they are spam ones or not. A series of numerical experiments on a real dataset acquired from Taobao.com have confirmed the effectiveness of the proposed model, not only reaping benefits in terms of time efficiency and high accuracy but also overcoming the shortcomings of supervised learning methods, which depend on large amounts of labeled examples. And a trade-off balance was obtained between accuracy and efficiency.

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Detecting singleton spams in reviews via learning deep anomalous temporal aspect-sentiment patterns

Cited by 9 publications

References 74 publications

Online Troll Reviewer Detection Using Deep Learning Techniques

Online Troll Reviewer Detection Using Deep Learning Techniques

Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data

Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning

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