Effective Bot Detection in Twitter using Deep Boltzmann Machine

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Vahid Maryam; Ravanmehr Reza

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2024 | Issue:10

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Title

Effective Bot Detection in Twitter using Deep Boltzmann Machine

Author(s)

Vahid Maryam | Ravanmehr Reza

Keywords

Social Networks

Botnet

Deep Neural Networks

Boltzmann Machine

Twitter

Abstract

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.

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APA: Copy

Vahid, Maryam, & Ravanmehr, Reza. (2024). Effective Bot Detection in Twitter using Deep Boltzmann Machine. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/1147395/en

Vancouver: Copy

Vahid Maryam, Ravanmehr Reza. Effective Bot Detection in Twitter using Deep Boltzmann Machine. 2024. Available from: https://sid.ir/paper/1147395/en

IEEE: Copy

Maryam Vahid, and Reza Ravanmehr, “Effective Bot Detection in Twitter using Deep Boltzmann Machine,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2024, [Online]. Available: https://sid.ir/paper/1147395/en

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