  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.
