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Vol. 244, Issue 5, September 2020, pp. 37-43




Bot Detection in Social Networks Based on Multilayered Deep Learning Approach

1 Sandeep Singh Sengar, 2 Sanjay Kumar, 2 Pradyot Raina and 2 Mukul Mahaliyan

1 Department of Computer Science and Engineering, SRM University-AP, Andhra Pradesh, India-522502
2 Department of Computer Science and Engineering, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, New Delhi, India-110042
E-mail: sandeep.iitdhanbad@gmail.com, sanjay.kumar@dtu.ac.in, pradyotrainaout@gmail.com, mukulmahaliyan@gmail.com


Received: 18 July 2020 /Accepted: 28 August 2020 /Published: 30 September 2020

Digital Sensors and Sensor Sysstems


Abstract: With the swift rise of social networking sites, they have now come to hold tremendous influence in the daily lives of millions around the globe. The value of one’s social media profile and its reach has soared highly. This has invited the use of fake accounts, spammers and bots to spread content favourable to those who control them. Thus, in this project we propose using a machine learning approach to identify bots and distinguish them from genuine users. This is achieved by compiling activity and profile information of users on Twitter and subsequently using natural language processing and supervised machine learning to achieve the objective classification. Finally, we compare and analyse the efficiency and accuracy of different learning models in order to ascertain the best performing bot detection system.


Keywords: Bot detection, Machine learning, Natural Language Processing, Social network, Text classification.


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