Enactment of Ensemble Learning for Review Spam Detection on Selected Features

Khurshid, Faisal; Zhu, Yuanrong; Xu, Zenglin; Ahmad, Mushtaq; Ahmad, Muqeet

doi:10.2991/ijcis.2019.125905655

Cited by 28 publications

(12 citation statements)

References 23 publications

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“…There are three types of machine learning: supervised, unsupervised, and semi-supervised. The commonly used supervised learning methods include support vector machines (SVM) [3], [24], [51]- [53], [27]- [29], [37], [47]- [50], logistic regression (LR) [27], [30]- [32], [53], Naïve Bayes (NB) [27]- [29], [33]- [36], [38], [51], [53], k-nearest neighbor (kNN) [28], [39], [51], decision trees (DT) [27], [40], [41], random forest (RF) [28], [29], [39], [42], [43], Adaptive Boosting (Adaboost) [44], [45], Sparse Additive Generative Model (SAGE) [46], and multilayer perceptron (MLP) [29].…”

Section: A Ml/dl-based Fake Reviews Detection Methodsmentioning

confidence: 99%

Fake Online Reviews: A Unified Detection Model Using Deception Theories

2022

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Online reviews influence consumers' purchasing decisions. However, identifying fake reviews online automatically remains a complex problem, and current detection approaches are inefficient in preventing the spread of fake reviews. The literature on fake reviews detection lacks a comprehensive and interpretable theory-based model with high performance, which enables us to understand the phenomenon from a psychological perspective and analyze reviews based on user-generated content as well as consumer behavior. In this research, we synthesized ten well-founded deception theories from psychology, namely leakage theory, four-factor theory, interpersonal deception theory, self-presentational theory, reality monitoring theory, criteria-based content analysis, scientific content analysis, verifiability approach, truthdefault theory, and information manipulation theory, and selected nine relevant constructs to develop a unified model for detecting fake online reviews. These constructs include specificity, quantity, nonimmediacy, affect, uncertainty, informality, consistency, source credibility, and deviation in behavior. We characterized the selected constructs using verbal and non-verbal features to validate the proposed model empirically. Subsequently, we extracted features from the Yelp datasets and used them to train four machine learning algorithms, specifically Logistic Regression, Naïve Bayes, Decision Tree, and Random Forest. We demonstrated that quantity, non-immediacy, affect, informality, consistency, source credibility, and deviation in behavior are essential constructs for detecting fake reviews. To our surprise, we discovered that non-verbal features are more important than verbal features and that combining features from both types improves the prediction performance. Our theory-based model outperformed most of the state-of-the-art fake review detection models and yielded high interpretability and low complexity.

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Section: A Ml/dl-based Fake Reviews Detection Methodsmentioning

confidence: 99%

Fake Online Reviews: A Unified Detection Model Using Deception Theories

2022

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“…Motivated by this, Khurshid, et al [18] extended their previous work and proposed an ensemble learning model to detect fake reviews based on selected features. The proposed model consisted of two tiers: Tier 1 used three classifiers (Discriminative Multi-nominal Naive Bayes, a library for Support Vector Machine and J48), and Tier 2 used Logistic Regression classifier to introduce an accurate result.…”

Section: ) Traditional Statistical Supervised Learning In Detecting Fake Rreviewsmentioning

confidence: 97%

Fake Reviews Detection: A Survey

et al. 2021

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In e-commerce, user reviews can play a significant role in determining the revenue of an organisation.Online users rely on reviews before making decisions about any product and service. As such, the credibility of online reviews is crucial for businesses and can directly affect companies' reputation and profitability. That is why some businesses are paying spammers to post fake reviews. These fake reviews exploit consumer purchasing decisions. Consequently, the techniques for detecting fake reviews have extensively been explored in the past twelve years. However, there still lacks a survey that can analyse and summarise the existing approaches. To bridge up the issue, this survey paper details the task of fake review detection, summing up the existing datasets and their collection methods. It analyses the existing feature extraction techniques. It also summarises and analyses the existing techniques critically to identify gaps based on two groups: traditional statistical machine learning and deep learning methods. Further, we conduct a benchmark study to investigate the performance of different neural network models and transformers that have not been used for fake review detection yet. The experimental results on two benchmark datasets show that RoBERTa performs about 7% better than the state-of-the-art methods in a mixed domain for the deception dataset with the highest accuracy of 91.2%, which can be used as a baseline for future studies. Finally, we highlight the current gaps in this research area and the possible future directions. INDEX TERMSFake review; Fake review detection; Feature engineering, Machine learning; Deep learning.

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“…Some other work in this field can be studied from Lin, Wang, Li, and Zhou (2015), , and Prabowo and Thelwall (2009). Khurshid, Zhu, Xu, Ahmad, and Ahmad (2018) have proposed an ensemble learning module with chi-squared feature selection. The model was built as two-tier having DMNB, J48 & LibSVM, and LR classifiers.…”

Section: Unsupervised and Semi-supervised Learningmentioning

confidence: 99%

Role of sentiment analysis in social media security and analytics

Sharma

Jain

2020

WIREs Data Min & Knowl

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Social media, in recent times, has with eased an explosion of data with so many social media platforms available to interact and express opinions freely. This has led to easy access to the privacy of social media users which raise broader security concerns and issues. The present paper provides an overview of various sentiment analysis approaches and techniques for social media security and analytics. The multiple security application domains like deception detection, anomaly detection, risk management, and disaster relief have been identified where sentiment analysis is used for social media security. An indepth study on security issues related to data provenance, distrust, e-commerce security, consumer security breaches, market surveillance, credibility, and risk assessment in social media have been presented. A comparison of various techniques, methodologies, dataset, and application domain where sentiment analysis is used has been discussed. The present work discusses the results of different machine learning techniques based on the performance metrics that have been used for the implementation of sentiment analysis in the respective security domains. It identifies the various gaps, issues, and the recent advancements in the field and presents a line of work that needs to be carried forward in future.

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Enactment of Ensemble Learning for Review Spam Detection on Selected Features

Cited by 28 publications

References 23 publications

Fake Online Reviews: A Unified Detection Model Using Deception Theories

Fake Online Reviews: A Unified Detection Model Using Deception Theories

Fake Reviews Detection: A Survey

Role of sentiment analysis in social media security and analytics

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