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CC BY V4.0, Open Access
Journal article
Social media bot detection with deep learning methods: A systematic review
Neural Computing and Applications
2023
Abstract
Social bots are automated social media accounts governed by software and controlled by humans at the backend. Some bots have good purposes, such as automatically posting information about news and even to provide help during emergencies. Nevertheless, bots have also been used for malicious purposes, such as for posting fake news or rumour spreading or manipulating political campaigns. There are existing mechanisms that allow for detection and removal of malicious bots automatically. However, the bot landscape changes as the bot creators use more sophisticated methods to avoid being detected. Therefore, new mechanisms for discerning between legitimate and bot accounts are much needed. Over the past few years, a few review studies contributed to the social media bot detection research by presenting a comprehensive survey on various detection methods including cutting-edge solutions like machine learning (ML)/deep learning (DL) techniques. This paper, to the best of our knowledge, is the first one to only highlight the DL techniques and compare the motivation/effectiveness of these techniques among themselves and over other methods, especially the traditional ML ones. We present here a refined taxonomy of the features used in DL studies and details about the associated pre-processing strategies required to make suitable training data for a DL model. We summarize the gaps addressed by the review papers that mentioned about DL/ML studies to provide future directions in this field. Overall, DL techniques turn out to be computation and time efficient techniques for social bot detection with better or compatible performance as traditional ML techniques.
Details
- Title
- Social media bot detection with deep learning methods: A systematic review
- Authors/Creators
- Kadhim HayawiSusmita SahaMohammad MasudSujith MathewMohammed G Kaosar (Author) - Murdoch University, Centre for Healthy Ageing
- Publication Details
- Neural Computing and Applications
- Publisher
- Springer-Verlag London Ltd., part of Springer Nature
- Identifiers
- 991005562370007891
- Copyright
- © 2023 The Authors.
- Murdoch Affiliation
- Centre for Healthy Ageing
- Language
- English
- Resource Type
- Journal article
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