Automatic Detection of Bot-generated Tweets
Open accessAbstract
Deep neural networks have the capacity to generate textual content which is increasingly difficult to distinguish from that produced by humans. Such content can be used in disinformation campaigns and its detrimental effects are amplified if it spreads on social networks. Here, we study the automatic detection of bot-generated Twitter messages. This task is difficult due to combination between the strong performance of recent deep language models and the limited length of tweets. In this study, we propose a challenging definition of the problem by making no assumption regarding the bot account, its network or the method used to generate the text. We devise two approaches for bot detection based on pretrained language models and create a new dataset of generated tweets to improve the performance of our classifier on recent text generation algorithms. The obtained results show that the generalization capabilities of the proposed classifier heavily depends on the dataset used to trained the model. Interestingly, the two automatic dataset augmentation proposed here show promising results. Their introduction leads to consistent performance gains compared to the use of the original dataset alone.
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Subsequent studies have built upon the Fagni et al. dataset to explore various strategies for bot detection based on content.
“Subsequent studies have built upon this dataset to explore various strategies for bot detection based on content (Saravani et al., 2021;Gambini et al., 2022;Tourille et al., 2022).”
From Anatomy of an AI-powered malicious social botnet (2024) · cites this paper as Tourille et al., 2022
Cites
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · see citing works
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821 · see citing works
Automatic Detection of Generated Text is Easiest when Humans are Fooled
Ippolito, D., Duckworth, D., Callison-Burch, C., & Eck, D. (2019) · arXiv · doi:10.48550/arxiv.1911.00650 · Cited by 264 · cited 1×
“This format is ideal for text generation algorithms because short texts written by bots are more difficult to distinguish from human-generated ones compared to longer texts [9].”
BackgroundAutomatic Detection of Generated Text is Easiest when Humans are Fooled
Ippolito, D., Duckworth, D., Callison-Burch, C., & Eck, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.164 · Cited by 264 · see citing works
DeBot: Twitter Bot Detection via Warped Correlation
Chavoshi, N., Hamooni, H., & Mueen, A. (2016) · 2016 IEEE 16th International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2016.0096 · Cited by 260 · see citing works
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A 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
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
Qureshi, S. M., Saeed, A., Almotiri, S. H., Ahmad, F., & Al Ghamdi, M. A. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2037 · Cited by 71 · see citing works
- Authors
- Tourille, J., Sow, B., & Popescu, A.
- Affiliations
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Université Clermont Auvergne (FR)
- Venue
- Proceedings of the 1st International Workshop on Multimedia AI against Disinformation
- Publisher
- ACM
- Published
- 2022-06-27
- Pages
- 44-51
- OpenAlex
- W4283464460
- Cited by
- 15
- References
- 5
- Citations by year
- 2026: 2 · 2024: 6 · 2023: 6 · 2022: 1
- FWCI
- 2.91
- Type
- conference paper
- Language
- en
- Open access
- green
- cea.hal.science
- Locations
- HAL (submittedVersion, other-oa)
- Funders
- European Commission (951911)
- Fondation MAIF
- Regional Council of Ile-de-France
- 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 › Authorship Attribution and Profiling
- Keywords
- bot detection, Twitter, disinformation, pre-trained language models, data augmentation, deep language models
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