2020/02/03 by Wei Feng, Feng Wei, Wei, Feng +2 · 18 citations
Computer Science · Social Sciences · #Artificial intelligence #Artificial neural network #Computation and Language (cs.CL) #Computer science #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Friendship #Long short term memory #Machine Learning (cs.LG) #Machine learning #Misinformation and Its Impacts #Network Security and Intrusion Detection #Popularity #Recurrent neural network #Social media #Spam and Phishing Detection #Term (time) #Word (group theory) #World Wide Web #cs.CL #cs.CR #cs.LG
paper · pdf · doi:10.48550/arxiv.2002.01336
published in arXiv (Cornell University) (Cornell University) · IEEE TPS 2019. arXiv admin note: text overlap with arXiv:1703.04482 by other authors
arxiv created 2020/02/03 · openalex publication_date 2020/02/03 · arxiv updated 2020/02/05 · openalex created_date 2020/02/14 · openalex updated_date 2026/08/08
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.