Malicious Account Detection on Twitter Based on Tweet Account Features using Machine Learning

Farhan Nurdiatama Pakaya, Muhammad Okky Ibrohim, Indra Budi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

As one of the most popular social media, Twitter is facing issues with the massive numbers of its users. This has led many to exploit the platform to perform cyber crime to other users. One of the cybercrime is the activity of malicious accounts. Malicious accounts such as spambots and fake followers can be problematic as they may harm other users. Spambots can send other users unwanted messages and fake followers can increase other accounts following numbers signaling trustworthiness or influence. Much research has been conducted to build a malicious account detector, but mostly use profile-based and graph-based features. On the other hand, malicious and genuine accounts can have distinct ways to tweet. In this research, we build a classification model using only account tweets. We also build further classification distinguishing fake followers and spambots from genuine accounts. In this research, maximum accuracy has been reached at 95.55% in malicious vs genuine account detection using tf-idf features and XGBoost algorithm and 95.2% in all three types of accounts using Word2Vec features and XGBoost algorithm.

Original languageEnglish
Title of host publicationProceedings of 2019 4th International Conference on Informatics and Computing, ICIC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728122076
DOIs
Publication statusPublished - Oct 2019
Event4th International Conference on Informatics and Computing, ICIC 2019 - Semarang, Indonesia
Duration: 23 Oct 201924 Oct 2019

Publication series

NameProceedings of 2019 4th International Conference on Informatics and Computing, ICIC 2019

Conference

Conference4th International Conference on Informatics and Computing, ICIC 2019
CountryIndonesia
CitySemarang
Period23/10/1924/10/19

Keywords

  • machine learning
  • malicious accounts
  • Twitter

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