BotSpot: Deep learning classification of bot accounts within Twitter

Christopher Braker*, Stavros Shiaeles, Gueltoum Bendiab, Nick Savage, Konstantinos Limniotis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The openness feature of Twitter allows programs to generate and control Twitter accounts automatically via the Twitter API. These accounts, which are known as “bots”, can automatically perform actions such as tweeting, re-tweeting, following, unfollowing, or direct messaging other accounts, just like real people. They can also conduct malicious tasks such as spreading of fake news, spams, malicious software and other cyber-crimes. In this paper, we introduce a novel bot detection approach using deep learning, with the Multi-layer Perceptron Neural Networks and nine features of a bot account. A web crawler is developed to automatically collect data from public Twitter accounts and build the testing and training datasets, with 860 samples of human and bot accounts. After the initial training is done, the Multi-layer Perceptron Neural Networks achieved an overall accuracy rate of 92%, which proves the performance of the proposed approach.

Original languageEnglish
Title of host publicationInternet of Things, Smart Spaces, and Next Generation Networks and Systems - 20th International Conference, NEW2AN 2020 and 13th Conference, ruSMART 2020, Proceedings
EditorsOlga Galinina, Sergey Andreev, Sergey Balandin, Yevgeni Koucheryavy
PublisherSpringer
Pages165-175
Number of pages11
ISBN (Electronic)9783030657260
ISBN (Print)9783030657253
DOIs
Publication statusPublished - 22 Dec 2020
Event20th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2020 and 13th Conference on the Internet of Things and Smart Spaces, ruSMART 2020 - St. Petersburg, Russian Federation
Duration: 26 Aug 202028 Aug 2020

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume12525
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2020 and 13th Conference on the Internet of Things and Smart Spaces, ruSMART 2020
Country/TerritoryRussian Federation
CitySt. Petersburg
Period26/08/2028/08/20

Keywords

  • Bot accounts
  • Machine learning
  • Security
  • Spam bots

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