Spam Reviews Research Articles

Fake Review Detection Using Machine learning and Deep Learning

The widespread adoption of Web 2.0 platforms has enabled consumers to share their opinions on products and services, influencing purchasing decisions. However, the proliferation of spam reviews has undermined the credibility of online reviews. This study aims to identify and evaluate existing approaches for detecting reviews, individual spammers, and Organizations. We categorized machine learning (ML) and deep learning (DL) techniques used for Review detection and assessed their effectiveness. Our findings indicate that accuracy is the most frequently used metric (25%), followed by recall (24%) and precision (22%). Additionally, we observed that utilizing the entire Amazon dataset can enhance the performance of F-measure, AUC, and F1-score metrics by 7%. Our study concludes that SMS spam filtering strategies are often effective in combating spam reviews. Furthermore, we developed a taxonomy of existing methodologies and observed a significant number of studies employing SMS anti-spam applications. This research uncovered innovative applications of ML and DL to spam review detection, offering a novel approach to addressing this issue. Our findings provide both academics and practitioners with a deeper understanding of the challenges in spam review identification and potential avenues for improvement using ML techniques.

NetSpam : A Network-Based Spam Detection Framework for Reviews in Online Social Media.

The system ensures about irrelevant or promotional comments on blogs, forums, or social media. In today’s digital age, online reviews play a crucial role in shaping consumer decisions and business reputations. However, the open nature of these platforms also provides opportunities for malicious actors to post spam reviews, misleading users and undermining the credibility of genuine feedback. Social media enables customers to share their views, opinions and experiences as product reviews. These product reviews facilitate customers in buying quality products. Due to the significance of online reviews, fake reviews, commonly known as spam reviews are generated to mislead the potential customers in decision-making. To cater this issue, review spam detection has become an active research area.

AI-Generated Spam Review Detection Framework with Deep Learning Algorithms and Natural Language Processing

Spam reviews pose a significant challenge to the integrity of online platforms, misleading consumers and undermining the credibility of genuine feedback. This paper introduces an innovative AI-generated spam review detection framework that leverages Deep Learning algorithms and Natural Language Processing (NLP) techniques to identify and mitigate spam reviews effectively. Our framework utilizes multiple Deep Learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), to capture intricate patterns in textual data. The system processes and analyzes large volumes of review content to detect deceptive patterns by utilizing advanced NLP and text embedding techniques such as One-Hot Encoding, Word2Vec, and Term Frequency-Inverse Document Frequency (TF-IDF). By combining three embedding techniques with four Deep Learning algorithms, a total of twelve exhaustive experiments were conducted to detect AI-generated spam reviews. The experimental results demonstrate that our approach outperforms the traditional machine learning models, offering a robust solution for ensuring the authenticity of online reviews. Among the models evaluated, those employing Word2Vec embeddings, particularly the BiLSTM_Word2Vec model, exhibited the strongest performance. The BiLSTM model with Word2Vec achieved the highest performance, with an exceptional accuracy of 98.46%, a precision of 0.98, a recall of 0.97, and an F1-score of 0.98, reflecting a near-perfect balance between precision and recall. Its high F2-score (0.9810) and F0.5-score (0.9857) further highlight its effectiveness in accurately detecting AI-generated spam while minimizing false positives, making it the most reliable option for this task. Similarly, the Word2Vec-based LSTM model also performed exceptionally well, with an accuracy of 97.58%, a precision of 0.97, a recall of 0.96, and an F1-score of 0.97. The CNN model with Word2Vec similarly delivered strong results, achieving an accuracy of 97.61%, a precision of 0.97, a recall of 0.96, and an F1-score of 0.97. This study is unique in its focus on detecting spam reviews specifically generated by AI-based tools rather than solely detecting spam reviews or AI-generated text. This research contributes to the field of spam detection by offering a scalable, efficient, and accurate framework that can be integrated into various online platforms, enhancing user trust and the decision-making processes.

Spam Review Detection Using Weighted Swarm Support Vector Machines and Pre-Trained Word Embedding for Multiple Languages

Before making a purchase, many find internet reviews to be a vital source of information. On top of that, companies may learn a lot about their products and services via these reviews. Having faith in these reviews was especially important during the COVID-19 pandemic, when many stayed inside and read reviews at a dizzying pace. The pandemic altered the atmosphere and people's preferences in addition to increasing the number of evaluations. Spam reviewers keep an eye on these changes and try to improve their sneaky techniques. In order to deceive customers or harm competitors, reviews that are deemed spam may include inaccurate, misleading, or dishonest information. Consequently, this work introduces a WSVM plus an HHO to identify spam reviews. The HHO is similar to an algorithm in that it optimises hyperparameters and uses feature weights. Using English, Spanish, and Arabic language corpora as datasets, the multilingual difficulty in spam reviews has been tackled. Ngram-3, TFIDF, whereas One-hot encoding are three methods for representing words, while pre-trained word incorporation (BERT) is another one that has been used. Each of the four such studies has shed light on and provided a solution to a different facet. From start to finish, the proposed technique beat rival cutting-edge algorithms in every test. For the Multi dataset, the WSVM-HHO achieved a success rate of 84.270 percent; for the English information set, 89.565 percent; for the Spanish information set, 71.913 percent; and for the Arabic dataset, 88.565 percent. Furthermore, we have extensively researched the review environment before to and during the COVID-19 event. To further enhance detection performance, it has been designed to merge its existing textual attributes with statistical information to build a new dataset.

Graph neural network-based attention mechanism to classify spam review over heterogeneous social networks

Graph neural network-based attention mechanism to classify spam review over heterogeneous social networks

Spam Review Detection using Machine Learning

Customer opinions play a vital role in buying decisions. These days most customers post their opinions about products on blogs, e-commerce sites, review sites, and social networking sites. The above information is consumed by business or corporate organizations, as they are eagerly interested in analyzing consumer views about their products, services, and support. As people buy products after reading the reviews, the kind of reviews that a product attracts are of concern to the sellers. This means that a positive review on the product would bring in sales and a negative one would reduce them. The project leverages Natural Language Processing (NLP) techniques and supervised learning algorithms to build a robust spam review detection system. The system is trained on a dataset comprising genuine and spam reviews, and it extracts various features from the textual content of reviews, such as sentiment analysis, linguistic patterns, and semantic meaning.

E-Commerce Spam Review Detection using Machine Learning

With the proliferation of e-commerce platforms, the authenticity of online reviews has become increasingly crucial. In this research, we present a method for detecting spam reviews in e-commerce platforms, employing the Random Forest algorithm. Leveraging a dataset comprising reviews from Amazon Yelp dataset, we employ a combination of Natural Language Processing (NLP) techniques and text processing methods to uncover underlying patterns distinguishing between genuine and fake reviews. Real-time data scraping from the Amazon website facilitated the acquisition of a diverse range of reviews, subsequently stored in a CSV file for analysis. These reviews stored in CSV file is fed to model for prediction. Our model effectively discerns between authentic and spam reviews, offering a valuable tool for maintaining the integrity of e-commerce platforms and ensuring informed consumer decision-making. Key Words: Web Scrapping, Text Mining, Sentiment Analysis.

A Weighted Stacking Ensemble Model With Sampling for Fake Reviews Detection

Customers use reviews as a primary source of information to judge a product or service. Positive reviews help boost companies’ reputations, increasing their revenue by attracting new clients, and increasing the purchasing order size. On the other hand, negative reviews significantly reduce sales, which might be the case due to competitive advantage. Organizations can use fake (i.e., misleading or fraudulent) reviews to generate fast profits by deceiving customers into buying their products. Recently, various methods to assess the legitimacy of reviews have been introduced using advances in machine learning. However, existing methods fall short of achieving highly accurate detection results for unbalanced classes. We aimed to create a spam review identification model using ensemble-based learning while balancing classes using sampling techniques. This article proposes a weighted stacking ensemble model with sampling (WSEM-S) for efficient fake reviews detection. We used <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$</tex-math> </inline-formula> -gram models to effectively model language data for feature retrieval. The experimental results on three customer reviews datasets: YELPNYC, Deceptive Opinion Spam Corpus (DOSC) v1.4, and Deception datasets show that the proposed model outperforms the conventional machine learning techniques Naïve Bayes, logistic regression, K-nearest neighbor (KNN), random forest, extreme gradient boosting (XGBoost), and convolutional neural network (CNN) as well as the state-of-the-art ensemble models.

Meta heuristic approaches for sentiment analysis

AbstractOn the Internet, online microblogging and social networks have experienced tremendous growth. Millions of individuals share opinions on social networking platforms including Twitter, Facebook, YouTube, and Microblogging sites. Millions of individuals express their thoughts on social platforms namely Twitter, Facebook, YouTube, and microblogging sites. As user‐generated content is growing to a huge extent on the internet, the analysis, extraction identification and classification of opinion and sentiment polarity of different aspects has now become a challenging issue in sentiment analysis. In sentiment analysis, aspect level opinion mining, opinion spam review detection, extraction and representation of most relevant accessed data from the user‐generated data is a difficult process. The word vector representation using existing deep learning techniques affects the performance in aspect level opinion mining. Spammers propagate false opinion for payment exchange, which leads to degradation of the financial growth, business, and fame of the organization. To overcome the limitations aspect level opinion mining is used to recognize sentiment and polarity of aspects in the specified text. This work presents various input feature vectors of deep learning approaches aiming to improve performance in aspect level opinion mining. Next, three meta‐heuristic algorithms with k‐means clustering approach for classification of opinion spam reviews on social media transit tweets is presented. The proposed approaches are evaluated and compared with other existing approaches as well as other benchmark datasets namely Restaurant and Amazon reviews.

Metadata Integration for Spam Reviews Detection on Vietnamese E-Commerce Websites

The problem of detecting spam reviews (opinions) has received significant attention in recent years, especially with the rapid development of e-commerce. Spam reviews are often classified based on comment content, but in some cases, it is insufficient for models to accurately determine the review label. In this work, we introduce the ViSpamReviews v2 dataset, which includes metadata of reviews with the objective of integrating supplementary attributes for spam review classification. We propose a novel approach to simultaneously integrate both textual and categorical attributes into the classification model. In our experiments, the product category proved effective when combined with deep neural network (DNN) models, while text features performed well on both DNN models and the model achieved state-of-the-art performance in the problem of detecting spam reviews on Vietnamese e-commerce websites, namely PhoBERT. Specifically, the PhoBERT model achieves the highest accuracy when combined with product description features generated from the SPhoBert model, which is the combination of PhoBERT and SentenceBERT. Using the macro-averaged F1 score, the task of classifying spam reviews achieved 87.22% (an increase of 1.64% compared to the baseline), while the task of identifying the type of spam reviews achieved an accuracy of 73.49% (an increase of 1.93% compared to the baseline).

Understanding Large-Scale Network Effects in Detecting Review Spammers

Opinion spam detection is a challenge for online review systems and social forum operators. Opinion spamming costs businesses and people money since it deceives customers as well as automated opinion mining and sentiment analysis systems by bestowing undeserved positive opinions on target firms and/or bestowing fake negative opinions on others. One popular detection approach is to model a review system as a network of users, products, and reviews, for example using review graph models. In this article, we study the effects of network scale on network-based review spammer detection models, specifically on the trust model and the SpammerRank model. We then evaluate both network models using two large publicly available review datasets, namely: the Amazon dataset (containing 6 million reviews by more than 2 million reviewers) and the UCSD dataset (containing over 82 million reviews by 21 million reviewers). It has been observed thatSpammerRank model provides a better scaling time for applications requiring reviewer indicators and in case of trust model distributions are flattening out indicating variance of reviews with respect to spamming. Detailed observations on the scaling effects of these models are reported in the result section.

Detecting Spam Reviews in Arabic by Deep Learning

Online reviews are frequently used by consumers to make decisions about online purchases, hotel bookings, car rentals, and other choices because online shopping has grown in popularity over the past few years. Reviews are now crucial to both the customer and the business. As writing fake reviews comes with financial gain, opinion spam activities have increased. Some unethical companies may hire workers to write reviews to influence consumers’ purchasing decisions; therefore, detecting spam reviews is a very important task. We compiled a large dataset of Arabic reviews consisting of spam and non-spam that are categorized by crowd-sourcing approach. Then, we applied deep learning algorithms to detect spam reviews. To the best of our knowledge, there are no prior studies utilized deep learning to classify reviews that are written in Arabic. Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) models were used, and an accuracy of 97% was achieved by both algorithms. To further improve the results, unbalanced issues were solved by oversampling and undersampling techniques. The results of them are improvements in the precision, recall, and F1-score for spam reviews. For example, in CNN F1-score for spam class increased from 79% to 90% with undersampling and became 82% with oversampling.

Fake reviews detection in e-commerce using machine learning techniques: A comparative survey

In the field of online commerce, customer reviews have great importance because they significantly influence the profits of a business. Most consumers on the internet rely on reviews to help them make decisions about what to buy because they provide a reliable way to read other people's opinions about a specific product. Since a company's reputation and profitability are directly impacted by the reliability of its online reviews, some business owners pay spammers to create fake reviews. The creation of fake reviews that influence consumers' purchase decisions is a persistent and detrimental problem. Therefore, developing techniques to help companies and customers to distinguish between genuine and fraudulent reviews are still an important but challenging task. As a result, this paper provides a survey on various machine learning techniques are proposed to deal with the problem of detecting fake reviews, as well as the performance of different techniques in spam review classification, and determine the features, strengths, and weaknesses of those methods that may require more development.

Online Detection and Infographic Explanation of Spam Reviews with Data Drift Adaptation

Online Detection and Infographic Explanation of Spam Reviews with Data Drift Adaptation

Spammer detection on short video applications

Users can interact with the advertisements and share their impressions through the review system on short video applications. However, spammers may post false or malicious comments to mislead normal users due to profit-driven reasons, damaging the community’s positive atmosphere. In this paper, we introduce a new challenge of spammer detection on short video applications, where the multi-modal information of videos and reviews plays a more critical role than the spam relation graph. Then we propose SPAM-3, a novel baseline to detect SPAM reviews with Multi-Modal representation using attentive heterogeneous graph convolution. Our approach balances multi-modal representation fusion and graph relation extraction, enabling fine-grained interaction and generating discriminative features for the spammer classification task. We describe the methodology of dataset construction in detail and reveal the statistical properties of the collected dataset. SPAM-3 outperforms the former baseline models on both private and public benchmarks. Furthermore, we conduct comprehensive ablations and analyses to demonstrate our method.

Identifying and Profiling Radical Reviewer Collectives in Digital Product Reviews

Ecommerce sites are flooded with spam reviews and opinions. People are usually hired to impede or promote particular brands by writing extremely negative or positive reviews. It is usually performed in groups. Various studies have been conducted to identify and scan those spam groups. However, there is still a knowledge gap when it comes to detecting groups targeting a brand, instead of products only. In this study, we conducted a systematic review of recent studies related to detection of extremist reviewer groups. Most of the researchers have extracted these groups with a data mining approach over brand similarities so that users are clustered. This study is an attempt to detect spammers with various models tested by various reviewers. This study presents proven conceptual models and algorithms which have been presented in previous studies to compute the spamming level of extremist reviewers in ecommerce sites and online marketplace.

Are Neighbors Alike? A Semisupervised Probabilistic Collaborative Learning Model for Online Review Spammers Detection

Online platforms continually face the menace of review spammers who manipulate ratings and comments for personal gain, eroding trust in online reviews. Despite various methods proposed to tackle this issue, challenges, like collusion between spammers, scarce labels, and skewed data distribution, persist. This paper introduces a practical solution to these problems by integrating individual behavioral features with a reviewer network, and it proposes a novel semisupervised collaborative learning model. Imagine our approach as a detective tool for online platforms. It works by investigating who is reviewing what and how they are linked within the reviewer network, which is like uncovering hidden relationships among reviewers to catch spammers. Through this method, our model significantly boosts the accuracy and transparency in identifying spammers. We tested this approach on real-world review data from different types of online platforms, and it outperformed other methods. Importantly, the model is resilient, even when you have limited data or when some labels might not be perfect. By using our approach, online platforms can spot fake reviews and spammers in a more cost-effective way, ensuring that the reviews are more reliable and that it is easier for consumers to make informed decisions. It is a practical step forward in the fight against fake reviews online.

A graph neural network approach to detect original review spammers of astroturfing campaigns

Online reviews affect consumers’ buying decisions. When astroturfing happens, the posted fake reviews not only cause confusion to the consumers, but in extreme cases, harm the reputation of others. Identifying the original posters of fake review in a discussion forum from the interactions with the peers in a collusive effort is essential to an overall detection strategy. However, existing detection approaches that focus on the mechanic characteristics of the posted reviews may fall short, since fake reviews cause more harm when astroturfing campaign happens involving multiple accomplices. We propose a detection framework with graph neural network, which incorporates the original perpetrator’s stylometric patterns and relationships with other accomplices. The framework was tested against the data collected from real incidents. Multiple deep learning models with fusion techniques were tested. Managerial and theoretical implications are provided.

Graph-Aware Deep Fusion Networks for Online Spam Review Detection

Product reviews on e-commerce platforms play a critical role in shaping users&#x2019; purchasing decisions. Unfortunately, online reviews sometimes can be intentionally misleading to manipulate the ecosystem. To date, existing methods to automatically detect &#x201C;spam reviews&#x201D; either focus on sophisticated feature engineering with traditional classification models or rely on tuning neural networks with aggregated features. In this article, we develop a novel graph-based model, namely, graph-aware deep fusion networks (GDFNs) that use information from relevant metadata (review text, features of users, and items) and relational data (network) to capture the semantic information from their complex heterogeneous interactions via graph convolutional networks (GCNs). Besides, GDFN also uses a novel fusion technique to synthesize low-and high-order interactions with propagated information across multiple review-related subgraphs. Extensive experiments on publicly available datasets show that our proposed model is effective and outperforms several strong state-of-the-art baselines.

Online consumer review spam detection based reinforcement learning and neural network

Online consumer review spam detection based reinforcement learning and neural network