Deep neural networks for bot detection
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Content posted by bots provides essential clues for bot detection.
“Content posted by bots also provides essential clues (Kudugunta and Ferrara, 2018;Heidari and Jones, 2020).”
From Anatomy of an AI-powered malicious social botnet (2024) · cites this paper as Kudugunta and Ferrara, 2018
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471 · see citing works
Samek, W., Wiegand, T., & Müller, K.-R. (2017) · arXiv · doi:10.48550/arxiv.1708.08296 · Cited by 1,080 · see citing works
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Varol, O., Ferrara, E., Davis, C., Menczer, F., & Flammini, A. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14871 · Cited by 928 · see citing works
Self-citationDetecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg?
Chu, Z., Gianvecchio, S., Wang, H., & Jajodia, S. (2012) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2012.75 · Cited by 634 · see citing works
Disinformation and social bot operations in the run up to the 2017 French presidential election
Ferrara, E. (2017) · First Monday · doi:10.5210/fm.v22i8.8005 · Cited by 524 · see citing works
Self-citation
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Arming the public with artificial intelligence to counter social bots
Yang, K., Varol, O., Davis, C. A., Ferrara, E., Flammini, A., & Menczer, F. (2019) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.115 · Cited by 419 · see citing works
“Recently, Kudugunta and Ferrara (2018) proposed a deep neural network approach based on the contextual long short-term memory (LSTM) architecture that exploits content and metadata to detect bots at the tweet level.”
BackgroundSelf-citationDetection 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
“Depending on the type of bots, similarity can be detected through tweet content [8, 25], temporal features in the timelines [7, 10], or retweeting behavior [28, 46].”
BackgroundWhat types of COVID-19 conspiracies are populated by Twitter bots?
Ferrara, E. (2020) · First Monday · doi:10.5210/fm.v25i6.10633 · Cited by 223 · see citing works
“centralized database to share annotated datasets of Twitter bots. Finally, various models have been proposed to detect bots using sophisticated machine learning techniques, such as: (1) Deep learning [53], (2) Anomaly detection [54]–[57], (3) Time series analysis [38], [58], [59]. Due to the continuously evolving natu…”
MethodSelf-citationA Survey on the Detection and Impacts of Deepfakes in Visual, Audio, and Textual Formats
Mubarak, R., Alsboui, T., Alshaikh, O., Inuwa-Dutse, I., Khan, S., & Parkinson, S. (2023) · IEEE Access · doi:10.1109/access.2023.3344653 · Cited by 132 · see citing works
Securing Industrial Internet of Things Against Botnet Attacks Using Hybrid Deep Learning Approach
Hasan, T., Malik, J., Bibi, I., Khan, W. U., Al-Wesabi, F. N., Dev, K., & Huang, G. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3168533 · Cited by 121 · see citing works
Anatomy of an AI-powered malicious social botnet
Yang, K., & Menczer, F. (2024) · Journal of Quantitative Description: Digital Media · doi:10.51685/jqd.2024.icwsm.7 · Cited by 71 · see citing works
Using Improved Conditional Generative Adversarial Networks to Detect Social Bots on Twitter
Wu, B., Liu, L., Yang, Y., Zheng, K., & Wang, X. (2020) · IEEE Access · doi:10.1109/access.2020.2975630 · Cited by 54 · see citing works
Emergent properties, models, and laws of behavioral similarities within groups of twitter users
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2020) · Computer Communications · doi:10.1016/j.comcom.2019.10.019 · Cited by 40 · see citing works
Fake Profile Detection Using Machine Learning Techniques
Chakraborty, P., Shazan, M. M., Nahid, M., Ahmed, M. K., & Talukder, P. C. (2022) · Journal of Computer and Communications · doi:10.4236/jcc.2022.1010006 · Cited by 32 · see citing works
Toward Mitigating Misinformation and Social Media Manipulation in LLM Era
Zhang, Y., Sharma, K., Du, L., & Liu, Y. (2024) · Companion Proceedings of the ACM Web Conference 2024 · doi:10.1145/3589335.3641256 · Cited by 25 · see citing works
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
Towards Detection of AI-Generated Texts and Misinformation
Najee-Ullah, A., Landeros, L., Balytskyi, Y., & Chang, S.-Y. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-10183-0_10 · Cited by 18 · see citing works
Investigating coordinated account creation using burst detection and network analysis
Bellutta, D., & Carley, K. M. (2023) · Journal of Big Data · doi:10.1186/s40537-023-00695-7 · Cited by 15 · see citing works
Instagram fake profile detection using an ensemble learning method
Goyal, B., Gill, N. S., Gulia, P., Alduaiji, N., Shukla, P. K., & J, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-03973-x · Cited by 10 · see citing works
- Authors
- Kudugunta, S., & Ferrara, E.
- Affiliations
- Indian Institute of Technology Hyderabad (IN)
- University of Southern California (US)
- Corresponding author
- Ferrara, Emilio
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 467
- Pages
- 312-322
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2787296320
- Cited by
- 506
- References
- 8
- Citations by year
- 2026: 33 · 2025: 57 · 2024: 64 · 2023: 81 · 2022: 71 · 2021: 80 · 2020: 79 · 2019: 35 · 2018: 6
- FWCI
- 80.64
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Information Sciences (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Defense Advanced Research Projects Agency (911NF-17-C-0094)
- Air Force Office of Scientific Research (9550-17-1-0327)
- 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
- bot detection, social bots, Twitter, user metadata, synthetic minority oversampling
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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