MSc.Thesis Defense:

Unsupervised Detection of Coordinated Fake Followers on Social Media

Yasser Zouzou
Data Science, MSc. Thesis, 2024

Thesis Jury

Asst. Prof.  Onur Varol (Thesis Advisor),

Assoc. Prof. Öznur Taştan,

Assoc. Prof. Hamid Akın Ünver

Date & Time: July 23rd, 2024 –  16:00 PM

Place: FASS G043

Zoom link: https://sabanciuniv.zoom.us/j/2534330944?omn=95416919198

Keywords: computational social science, fake-followers, bots, online coordinated activities

Abstract

Automated social media accounts, known as bots, are increasingly recognized as key tools for manipulative online activities. These activities can stem from coordination among several accounts and these automated campaigns can manipulate social network structure by following other accounts, amplifying their content, and posting messages to spam online discourse. In this study, we present a novel unsupervised detection method to target a specific category of malicious accounts designed to manipulate user metrics such as online popularity. Our framework identifies anomalous following patterns among all social media account followers.

Through the analysis of a large number of accounts on the Twitter platform (rebranded as X after the acquisition of Elon Musk), we demonstrated that irregular following patterns are prevalent and are indicative of automated fake accounts. Notably, we found that these detected groups of anomalous followers exhibited consistent behavior across multiple accounts. This observation, combined with the computational efficiency of our proposed approach, makes it a valuable tool for investigating large-scale coordinated manipulation campaigns on social media platforms.