Detection of Bots on Social Networks: A Review

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Sheyegan Mohammad Javad; Mazinani Javad

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2023 | Issue:9

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Title

Detection of Bots on Social Networks: A Review

Author(s)

Sheyegan Mohammad Javad | Mazinani Javad

Keywords

Interactive Bots

Fake Interactive Users

Fake Engagement Bots

Fake Advertising Bots

Fake Social Media Influencers

Abstract

There is no doubt that social networks have gained prominence among users in today's society. Having received special attention from users, social network marketing has attracted the attention of businesses and individuals. As a consequence of the competition in these spaces, businesses and individuals have used some incorrect methods in order to falsely increase the engagement rate of their websites. The use of bots has been one of the most common methods of falsely increasing engagement rates. In order to discover these fake robots, various researches have been conducted. The objective of this article is to provide a review of previous research in the field of detecting robots in social networks. Based on the results of the review, it was found that random forest, nearest neighbor, logistic regression, and linear regression algorithms are more widely used than other traditional machine learning approaches. As well, convolutional neural network algorithms, long-term memory, and transformer learning have become more popular in deep learning approaches.

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APA: Copy

Sheyegan, Mohammad Javad, & Mazinani, Javad. (). . . SID. https://sid.ir/paper/1046874/en

Vancouver: Copy

Sheyegan Mohammad Javad, Mazinani Javad. . . Available from: https://sid.ir/paper/1046874/en

IEEE: Copy

Mohammad Javad Sheyegan, and Javad Mazinani, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1046874/en

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