Book ChapterDOI
Social Context Based Naive Bayes Filtering of Spam Messages from Online Social Networks
Cinu C. Kiliroor,C. Valliyammai +1 more
- pp 699-706
TLDR
Experimental evaluations and comparisons prove that the proposed system with SLC factors provides a higher accuracy than accuracy of basic classifier.Abstract:
Nowadays, online social networking (OSN) sites are an inevitable way of communication. Almost all the OSNs have attained an explosive growth in the last few years. Spammers or hackers found that OSN is an important and easy way to spread spam messages over the network because of its popularity. Spammers use different strategies to spread spam. So, spam detection must be strong enough to detect spam effectively. Though several spam detection techniques are available, it is necessary to increase the accuracy of spam detection techniques. In this paper, a spam detection technique is proposed to detect and prevent spam messages. In addition to the usage of basic classifiers, social context features such as shares, likes, comments (SLC) are also done. Experimental evaluations and comparisons prove that the proposed system with SLC factors provides a higher accuracy than accuracy of basic classifier.read more
Citations
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Journal ArticleDOI
Twitter spam account detection based on clustering and classification methods
TL;DR: This paper proposes a different approach to detect spammers on Twitter based on the similarities that exist among spam accounts, and results revealed that Random Forest achieved the highest accuracy, precision, recall, and F -measure.
Journal ArticleDOI
Interaction-Based Behavioral Analysis of Twitter Social Network Accounts
Hafzullah İş,Taner Tuncer +1 more
TL;DR: This article considers methodological approaches to determine and prevent social media manipulation specific to Twitter by using k-nearest neighbor (K-NN), support vector machine (SVM), and artificial neural network (ANN) algorithms.
Journal ArticleDOI
BERT- and CNN-based TOBEAT approach for unwelcome tweets detection
TL;DR: A new approach is proposed that considers the extraction of new TOpics-Based fEAtures (TOBEAT), from Twitter data based on BERT (bidirectional encoder representations of transformers) and CNN (convolutional neural network) and shows that CNN is the most suitable classifier to solve the spam filtering task.
Journal ArticleDOI
A Profile Analysis of User Interaction in Social Media Using Deep Learning
Hafzullah İş,Taner Tuncer +1 more
TL;DR: The methodology demonstrated that Twitter user profiles can be classified successfully through user interaction-based parameters and is expected that this paper will contribute to published literature in terms of behavioral analysis and the determination of malicious accounts in social networks.
Journal ArticleDOI
Effective Filtering of Unsolicited Messages from Online Social Networks Using Spam Templates and Social Contexts
C. Valliyammai,Cinu C. Kiliroor +1 more
TL;DR: Experimental results demonstrate that the proposed model with SVM-Polynomial Radial Basis kernel which provides better accuracy in spam classification and outperforms all the state-of-the-art methods.
References
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Journal ArticleDOI
A Performance Evaluation of Machine Learning-Based Streaming Spam Tweets Detection
Chao Chen,Jun Zhang,Yi Xie,Yang Xiang,Wanlei Zhou,Mohammad Mehedi Hassan,Abdulhameed Alelaiwi,Majed Alrubaian +7 more
TL;DR: The results show the streaming spam tweet detection is still a big challenge and a robust detection technique should take into account the three aspects of data, feature, and model, and a performance evaluation of existing machine learning-based streaming spam detection methods is needed.
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A survey of data mining and social network analysis based anomaly detection techniques
Ravneet Kaur,Sarbjeet Singh +1 more
TL;DR: The paper presents a review of number of data mining approaches used to detect anomalies and a special reference is made to the analysis of social network centric anomaly detection techniques, broadly classified as behavior based, structure based and spectral based.
Journal ArticleDOI
Recent developments in social spam detection and combating techniques
TL;DR: The article surveys recent developments on social spam detection and mitigation, its theoretical models and applications along with their qualitative comparison, and presents the state-of-the-art and attempt to provide challenges to be addressed, as the nature and content of spam are bound to get more complicated.
Journal ArticleDOI
An Access Control Model for Online Social Networks Using User-to-User Relationships
TL;DR: A novel user-to-user relationship-based access control (UURAC) model for OSN systems that utilizes regular expression notation for such policy specification is proposed and validated by implementing a prototype system and evaluating the performance of these two algorithms.
Journal ArticleDOI
Co-detecting social spammers and spam messages in microblogging via exploiting social contexts
TL;DR: The proposed unified approach utilizes the posting relations between users and messages to combine social spammer detection with spam message detection and introduces an efficient optimization algorithm to solve the model of the approach and proposes an accelerated method to tackle the most time-consuming step.