Social Context Based Naive Bayes Filtering of Spam Messages from Online Social Networks

Kiliroor, Cinu C.; Valliyammai, C.

doi:10.1007/978-981-13-0514-6_66

Cited by 11 publications

(2 citation statements)

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“…It is important to prevent these attacks as they lead to manipulation that may have very negative political, social and economic consequences. There are many studies focusing on detecting malicious accounts, such as spam, bots and sybil attacks in social networks, and almost all of these studies use machine learning methods [8][9][10][11][12][13][14].…”

Section: Related Workmentioning

confidence: 99%

A Profile Analysis of User Interaction in Social Media Using Deep Learning

İş¹,

Tuncer²

2021

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It is highly important to detect malicious account interaction in social networks with regard to political, social and economic aspects. This paper analyzed the profile structure of social media users using their data interactions. A total of 10 parameters including diameter, density, reciprocity, centrality and modularity were used to comprehensively characterize the interactions of Twitter users. Moreover, a new data set was formed by visualizing the data obtained with these parameters. User profiles were classified using Convolutional Neural Network models with deep learning. Users were divided into active, passive and malicious classes. Success rates for the algorithms used in the classification were estimated based on the hyper parameters and application platforms. The best model had a success rate of 98.67%. The methodology demonstrated that Twitter user profiles can be classified successfully through user interaction-based parameters. It is expected that this paper will contribute to published literature in terms of behavioral analysis and the determination of malicious accounts in social networks.

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Section: Related Workmentioning

confidence: 99%

A Profile Analysis of User Interaction in Social Media Using Deep Learning

İş¹,

Tuncer²

2021

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“…The suggested method was based on the identifier, regular expression, and descriptive state machines. In order to improve the accuracy of the spam detection system, Kiliroor et al proposed a system to detect and prevent spam based on a naive Bayes spam filtering approach [28]. In the study, social context features such as stocks, likes, and comments were used to evaluate the performance of proposed models.…”

Section: Related Workmentioning

confidence: 99%

Interaction-Based Behavioral Analysis of Twitter Social Network Accounts

İş

Tuncer

2019

Applied Sciences

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This article considers methodological approaches to determine and prevent social media manipulation specific to Twitter. Behavioral analyses of Twitter users were performed by using their profile structures and interaction types, and Twitter users were classified according to their effect size values by determining their asset values. User profiles were classified into three different categories, namely popular-active, observer-passive, and spam-bot-malicious by using k-nearest neighbor (K-NN), support vector machine (SVM), and artificial neural network (ANN) algorithms. For classification, the study used the basic characteristics of users, such as density, centralization, and diameter, as well as suggested time series such as the simple moving average and cumulative moving average. The highest accuracy was obtained by the K-NN algorithm. The results obtained with K-NN for all classes were higher than the F1-Score values obtained for the other algorithms. According to the results obtained, classification accuracy values were found to reach a maximum of 96.81% and a minimum of 92.33%. Our classification results showed that the proposed method was satisfactory for popular-active, observer-passive, and spam-bot-malicious account separation.

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