Detecting Social Bots on Twitter: A Literature Review
Abstract
Due to the exponential growth in the popularity of online social networks (OSNs), such as Twitter and Facebook, the number of machine accounts that are designed to mimic human users has increased. Social bots accounts (Sybils) have become more sophisticated and deceptive in their efforts to replicate the behaviors of normal accounts. As such, there is a distinct need for the research community to develop technologies that can detect social bots. This paper presents a review of the recent techniques that have emerged that are designed to differentiate between social bot account and human accounts. We limit the analysis to the detection of social bots on the Twitter social media platform. We review the various detection schemes that are currently in use and examine common aspects such as the classifier, datasets, and selected features employed. We also compare the evaluation techniques that are employed to validate the classifiers. Finally, we highlight the challenges that remain in the domain of social bot detection and consider future directions for research efforts that are designed to address this problem.
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Supervised bot detection methods achieve high accuracy based on in-domain cross-validation.
“Supervised bot detection methods achieve high accuracy based on in-domain cross-validation [4].”
From Detection of Novel Social Bots by Ensembles of Specialized Classifiers (2020) · cites this paper as [4]
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Detection of Novel Social Bots by Ensembles of Specialized Classifiers
Sayyadiharikandeh, M., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management · doi:10.1145/3340531.3412698 · Cited by 159 · see citing works
“ial bots and are described in detail in the literature [42, 43, 48]. 2.2 Cross-domain performance comparison Supervised bot detection methods achieve high accuracy based on in-domain cross-validation [4]. To measure how recall deteriorates in cross-domain evaluation, we perform an experiment using four datasets select…”
MethodInfluentialUsing Improved Conditional Generative Adversarial Networks to Detect Social Bots on Twitter
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Automated Identification of Social Media Bots Using Deepfake Text Detection
Saravani, S. M., Ray, I., & Ray, I. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-92571-0_7 · Cited by 18 · see citing works
- Authors
- Alothali, E., Zaki, N., Mohamed, E. A., & Alashwal, H.
- Affiliations
- United Arab Emirates University (AE)
- Ajman University (AE)
- Venue
- 2018 International Conference on Innovations in Information Technology (IIT)
- Publisher
- IEEE
- Published
- 2018-11
- Pages
- 175-180
- OpenAlex
- W2910369730
- Semantic Scholar
- 9ff12871cecce8e6562286b68c69df026c443ca1
- Cited by
- 102
- References
- 45
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 18 · 2023: 18 · 2022: 17 · 2021: 20 · 2020: 11 · 2019: 4
- FWCI
- 6.87
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Innovations in Information Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- social bot detection, Twitter, human vs bot classification, classifier evaluation, detection dataset
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