Effective Bot Detection in Twitter using Deep Boltzmann Machine

Today, social networks have attracted the attention of billions of Internet users. On the other hand, the widespread use of these networks is susceptible to many dangerous purposes, such as spreading malware, stealing user information, spreading false information, etc. In this article, the effective...

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Bibliographic Details
Published in2024 10th International Conference on Web Research (ICWR) pp. 303 - 308
Main Authors Vahid, Maryam, Ravanmehr, Reza
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.04.2024
Subjects
Blogs
Boltzmann Machine
Botnet
Chatbots
Classification algorithms
Deep Neural Networks
Feature extraction
Malware
Social networking (online)
Social Networks
Support vector machines
Twitter
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Summary:Today, social networks have attracted the attention of billions of Internet users. On the other hand, the widespread use of these networks is susceptible to many dangerous purposes, such as spreading malware, stealing user information, spreading false information, etc. In this article, the effective detection of bots in the Twitter social network is proposed using the deep Boltzmann machine, which is one of the important types of deep neural networks. Indeed, various methods have been provided to detect bots in social networks. It is essential to extract key features that directly impact the accuracy of the methods. In order to achieve this goal, the Boltzmann machine neural network has been developed to extract the key and important features from the bunch of features included in the Twitter dataset. Then, based on the selected features, bots are detected using different classification approaches such as the K-nearest neighbor, support vector machine, AdaBoost, and decision tree, which provide better performance than the existing methods.
ISSN:2837-8296
DOI:10.1109/ICWR61162.2024.10533382