Open AccessDeepbot: A Deep Neural Network based approach for Detecting Twitter Bots
Author(s) -
Linhao Luo,
Xiaofeng Zhang,
Xiaofei Yang,
Weihuang Yang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/719/1/012063
Subject(s) - computer science , task (project management) , variety (cybernetics) , world wide web , service (business) , interface (matter) , artificial neural network , social network (sociolinguistics) , social media , artificial intelligence , computer security , internet privacy , engineering , economy , systems engineering , bubble , maximum bubble pressure method , parallel computing , economics
Social networks have played a very critical role in very aspect of our daily life. However, a wide variety of bots have been found which are designed for some malicious purposes such as spreading spam mes- sages and faking news. Although various techniques have been proposed, this task is still challenging if we want to judge whether the tweets are posted by a bot or not merely based on the textual information. For this challenge, the Deepbot is designed which adopts the Bi-LSTM model to analyze tweets and a Web interface is provided for public access which is developed using Web service. From our empirical studies, this system can achieve better classification accuracy.