E-Locus - Institutional Repository of the University of Crete

Home Collections School/Department School of Sciences and Engineering Department of Computer Science Doctoral theses

Doctoral theses

Current Record: 1 of 121

[Add to Basket]

Identifier

000462964

Title

The blue pill of matriX : analysis and prediction of twitter bots via explainable AI.

Alternative Title

Ανάλυση και πρόβλεψη των κακόβουλων λογαριασμών στο twitter μέσω ερμηνεύσιμης τεχνητής νοημοσύνης

Author

Shevtsov, Alexander S

Thesis advisor

Ιωαννίδης, Σωτήρης

Reviewer

Πρατικάκης, Πολύβιος
Τζίτζικας, Ιωάννης
Καρπαβέλος, Αλέξανδρος
Δημητρόπουλος, Ξενοφώντας
Κομοδάκης, Νίκος
Τσαγκατάκης, Γρηγόριος

Abstract

Online social media have become a primary source of information, achieving rapid content spreading across the globe. However, the content of online social media has raised concerns about fake news spreading and user misinformation. Malicious users, known as bot accounts, are able to exploit current online social media towards public opinion manipulation via the spread of fake or biased information in a convincing manner. Due to Twitter’s popularity and data access policies via API, it has become popular with malicious actors. During the past decade researchers have shown cases of propaganda, opinion manipulation, and influence during political events on Twitter. Measuring the direct impact of bot activity on human decisions is a challenging task. However, research has shown indirect correlations between bot activity and event outcomes, such as in the 2016 US Presidential Elections and stock market share prices. In order to preserve online social media integrity, effective bot detection methods are necessary. Current solutions lack effective bot detection in the general case and mostly offer high performance on very specific case scenarios, making them impractical in realworld situations. In this dissertation, we explore the evolution and challenges of the bot detection techniques, towards the creation of a novel general bot detection method (BotArtist). This method minimizes the utilization of the Twitter API usage and outperforms existing stateof-the-art bot detection methods. Towards this goal, we successfully identified botnet communities that share nearly identical content at nearly the same time. We meticulously analyzed a variety of characteristics and evaluated the differences between normal and bot accounts. Additionally, we applied the lessons we learned to develop machine learning models capable of bot detection within specific discussion topics. We also conducted an extensive evaluation of various account characteristics in terms of their predictive power over short and long time periods. This comprehensive approach led to the creation of BotArtist, currently the most versatile bot detection method. BotArtist has achieved remarkable results, with average F1 scores of 83.19 for specific dataset applications and 68.5 for general use cases. This represents an overall performance increase of nearly 10% compared to the current state-of-theart methods.

Language

English

Subject

Explainable AI

Social media analytics

Ερμηνεύσιμη τεχνητή νοημοσύνη

Issue date

2024-03-22

Collection

School/Department--School of Sciences and Engineering--Department of Computer Science--Doctoral theses

Type of Work--Doctoral theses

Permanent Link