Abstract

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.

Details

Title
Deepbot: A Deep Neural Network based approach for Detecting Twitter Bots
Author
Luo, Linhao 1 ; Zhang, Xiaofeng 1 ; Yang, Xiaofei 1 ; Yang, Weihuang 1 

 Department of Computer Science Harbin Institute of Technology Shenzhen, China 
Publication year
2020
Publication date
Jan 2020
Publisher
IOP Publishing
ISSN
17578981
e-ISSN
1757899X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2561927435
Copyright
© 2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.