Fake Accounts Detection on Social Media using Machine Learning: Review

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Mohammadi Dargah Yasaman; Dadkhah Chitra; Rezaei Niloofar

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Seminar Paper

Paper Information

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

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Title

Fake Accounts Detection on Social Media using Machine Learning: Review

Author(s)

Mohammadi Dargah Yasaman | Dadkhah Chitra | Rezaei Niloofar

Keywords

Social Network

Spammer

Fake Account

Detection

Machine Learning

Deep Learning

Abstract

In today society, Social Networking websites have drowned a remarkable attention from users ranging from a child to an old aged person all around the world. The community consumes a huge amount of time on online Social Networks by interacting and exchanging their information with the other people in the globe. As a result, some of the popular websites like Facebook, Twitter, Instagram, and others witnessed an unexpected growth in registered users. Meanwhile, researches exhibits that all registered accounts are not real,there exist a huge number of Fake Accounts created for a specific purpose. The major purpose of creating Fake Accounts is to spread spam content, rumor, and other unauthentic messages on the platforms. This leads to a motivation of developing a system that is able to identify and filter Fake Accounts on the Social Networks, but it has many challenges. Researchers have proposed several advanced algorithms to recognize Fake Accounts. In this paper, the development of Fake Account Detection algorithms using various Machine Learning approach and Deep Learning algorithms are reviewed, which give an open vision to the future researchers to develop a foundation in this field.

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APA: Copy

Mohammadi Dargah, Yasaman, Dadkhah, Chitra, & Rezaei, Niloofar. (). . . SID. https://sid.ir/paper/1046829/en

Vancouver: Copy

Mohammadi Dargah Yasaman, Dadkhah Chitra, Rezaei Niloofar. . . Available from: https://sid.ir/paper/1046829/en

IEEE: Copy

Yasaman Mohammadi Dargah, Chitra Dadkhah, and Niloofar Rezaei, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1046829/en

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