Opinion Spam Detection in Social Networks

Fei, G.; Li, H.; Liu, Bing

doi:10.1016/b978-0-12-804412-4.00009-7

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…The clever user intentionally enters a large number of URLs in their tweets to trap the legitimate users as their soft target. The URL ratio can be calculated using equation (2). =…”

Section: Mention Ratio / Url As Content-based Featuresmentioning

confidence: 99%

“…The social media communities are more liberal on their community standards and generally groups are formed between those users, who are more active and share information frequently compared to less active users. The unsolicited users enter to this chain of active users to execute their malicious activities [2]. Hackers possess as original users can have easy access to the important personal information such as bank account or passwords, available in social media account or those available in computing device (computer or mobile phone) [3].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning based Optimization Scheme for Detection of Spam and Malware Propagation in Twitter

Sheoran¹,

Yadav²

2021

IJACSA

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Social networking sites are new generation of webservices providing global community of users in an online environment. Twitter is one of such popular social networks having more than 152 million daily active users making a half billions of tweets per day. Owing to its immense popularity, the accounts of legitimate Twitter users are always at a risk from spammers and hackers. Spam and Malware are the most affecting threats reported on the Twitter platform. To preserve the privacy and ensure data safety for online Twitter community, it is necessary develop a framework to safeguard their accounts from such malicious attackers. Machine Learning is recently matured and widely used technique useful to prevent the propagation of such malicious activities in social media. However, the Machine Learning based techniques have yielded a promising result in filtering the undesired contents from the user tweets but its efficiency always remains restricted within the technological limits of the technique used. To devise a more efficient model to detect propagation of spam and malware in the Twitter, this research has proposed a Machine Learning based optimization scheme based on hybrid similarity (Cosine and Jaccard) measured in conjunction with Genetic Algorithm (GA) and Artificial Neural Network (ANN). The Cosine with Jaccard in hybridization has been applied on the Twitter dataset to identify the tweets containing spam and malware. In conjunction to it the GA has been used to enhance the training rate and reduce training error by automatically selecting the designed fitness function while the ANN was applied to classify malicious tweets from through voting rule. The simulation experiments were conducted to compute the precision rate, recall, F-measures. The results of Machine Learning based optimization scheme proposed in this research were compared with the existing stateof-arts techniques already available in this regime. The comparative study reveals that the model proposed in this research is faster and more precise then the existing models.

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“…The clever user intentionally enters a large number of URLs in their tweets to trap the legitimate users as their soft target. The URL ratio can be calculated using equation (2). =…”

Section: Mention Ratio / Url As Content-based Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning based Optimization Scheme for Detection of Spam and Malware Propagation in Twitter

Sheoran¹,

Yadav²

2021

IJACSA

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“…Supervised learning techniques that have been used for spam review detection so far are; Rule based classification [5,10], Unified model [2], Logistic Regression [4,11,12], Knearest neighbor (KNN) [4], Random Forest [4,[13][14][15], Decision Trees [16,17], Gradient Decent [4,10], Genetic Algorithm [18], Conceptual Model [19], Time Series [20], Neural Network [21], Deep Neural Network [22], Multinomial Naïve Bayes [9,11,13], N-Gram [13], Hybrid Learning Approach (Active and supervised learning) [23], RNN, CNN [24], and Multilayer Perceptron Model (MLP) [4,24], Unsupervised learning is a category of machine learning that work on the unlabeled datasets. Many unsupervised learning techniques have been used in spam detection which are: Natural Language Processing [6,9][58] Markov Network [25], Neural Auto-encoder Decision Forest [16]¸ and PU Learning [26]. Other than these supervised and unsupervised learning techniques, there are many other techniques that have been used for spam detection such as Fuzzy Logic [27], Heterogeneous Information Network [28], Hadoop [29], Text Mining [30], Sentiment Analysis [31][32][33][34]<...>…”

Section: Figure 1 Types Of Spammentioning

confidence: 99%

Spam Review Detection:A Systematic Literature Review

Farooq¹

2020

Preprint

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In this era of technology, people rely on online posted reviews before buying any product. These reviews are very important for both the consumers and people. Consumers and people use this information for decision making while buying products or investing money in any product. This has inclined the spammers to generate spam or fake reviews so that they can recommend their products and beat the competitors. Spammers have developed many systems to generate the bulk of spam reviews within hours. Many techniques, strategies have been designed and recommended to resolve the issue of spam reviews. In this paper, a complete review of existing techniques and strategies for detecting spam review is discussed. Apart from reviewing the state-of-the-art research studies on spam review detection, a taxonomy on techniques of machine learning for spam review detection has been proposed. Moreover, its focus on research gaps and future recommendations for spam review identification.

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“…Spam traffic specifications vary from authorized traffic authorization specifications. Authorized letters are usually issued throughout the day, while spamming is uniform throughout the day [2]. Spammers keep their identities secret when they send spam, but their identities are detectable when hunting mail addresses from websites, and this is one way of identifying spammers on the Internet.…”

Section: Literature Reviewmentioning

confidence: 99%

Spam Detection from Big Data based on Evolutionary Data Mining Systems

2018

Trans. Mach. Intell.

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News releases and users' ability to discuss events, events, and writing personalities and environments are services that provide opportunities for new types of spam and spammers. For example, popular topics and topics that involve the most discussions can be an opportunity to create traffic, visits, and sources of income. When something happens, thousands of users write about it, send text and quickly become the subject of discussion. These topics are targeted by spammers, because their writings contain the common words used in popular discussions. Often there are links in spam that direct users to websites that are not related to the topic, and since these URLs are shortened, it's difficult for users to log in. This type of spams can reduce the value and efficiency of instantaneous search services, and users of these services refer to materials that do not contain links to the searcher, so a method for identifying spammers should be found. Methods available to deal with spammers can be included in three categories which contain detection-based approach, prevention-based approach, and degradation-based approach that this research uses is a detection approach. Hence, this research uses a smart method that initially enters large data into the program, then a feature extraction based on the genetic algorithm is performed. In the next step, the classification of data in order to detect spam is done using the combined method of self-organized mapping neural network and probabilistic neural network with the support vector machine core as a radial basis function.

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Opinion Spam Detection in Social Networks

Cited by 7 publications

References 2 publications

Machine Learning based Optimization Scheme for Detection of Spam and Malware Propagation in Twitter

Machine Learning based Optimization Scheme for Detection of Spam and Malware Propagation in Twitter

Spam Review Detection:A Systematic Literature Review

Spam Detection from Big Data based on Evolutionary Data Mining Systems

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