Twitter Bot Detection Using Bidirectional Long Short-Term Memory Neural Networks and Word Embeddings
Open accessAbstract
Twitter is a web application playing dual roles of online social networking and micro-blogging. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots. Legitimate bots generate a large amount of benign contextual content, i.e., tweets delivering news and updating feeds, while malicious bots spread spam or malicious contents. To assist human users in identifying who they are interacting with, this paper focuses on the classification of human and spambot accounts on Twitter, by employing recurrent neural networks, specifically bidirectional Long Short-term Memory (BiLSTM), to efficiently capture features across tweets. To the best of our knowledge, our work is the first that develops a recurrent neural model with word embeddings to distinguish Twitter bots from human accounts, that requires no prior knowledge or assumption about users' profiles, friendship networks, or historical behavior on the target account. Moreover, our model does not require any handcrafted features. The preliminary simulation results are very encouraging. Experiments on the cresci-2017 dataset show that our approach can achieve competitive performance compared with existing state-of-the-art bot detection systems.
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Integrating models such as BERT or LSTM would have necessitated substantial changes to the data preprocessing pipeline and model architecture, diverging from the study's focus on metadata-based fake profile detection.
“Additionally, integrating models such as BERT or LSTM would have necessitated substantial changes to the data preprocessing pipeline and model architecture, diverging from the study’s focus on metadata-based fake profile detection.”
From Instagram fake profile detection using an ensemble learning method (2025) · cites this paper as [61]
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Towards Detection of AI-Generated Texts and Misinformation
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- Authors
- Wei, F., & Nguyen, U. T.
- Affiliations
- York University (CA)
- ORCID
- Nguyen, Uyen Trang
- Venue
- 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)
- Publisher
- IEEE
- Published
- 2019-12
- Pages
- 101-109
- OpenAlex
- W3010115535
- Cited by
- 168
- References
- 57
- Citations by year
- 2026: 19 · 2025: 33 · 2024: 34 · 2023: 35 · 2022: 22 · 2021: 20 · 2020: 5
- FWCI
- 9.36
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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
- Twitter bot detection, human vs bot classification, bidirectional LSTM, word embeddings, recurrent neural networks, social bots
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