Twitter Data Analysis for BOT Classification

Authors

  • Shweta Patil  Department of Computer Engineering, Zeal College of Engineering and Research,Narhe, Pune, Maharashtra, India
  • Aparna V. Mote  Department of Computer Engineering, Zeal College of Engineering and Research,Narhe, Pune, Maharashtra, India

Keywords:

Twitter, Twitter Bots, Metadata, Feature engineering, Machine Learning, Classification.

Abstract

With millions of users, Twitter is one of the most well-known microblogging platforms. Users are free to write about anything they like, including politics, sports, culinary, fashion, etc. Various assaults, including the dissemination of disinformation, phishing links, and malware, have targeted Twitter. Tweets must be posted by actual people and not by Twitter bots. The existing approaches leverage the user's tweets to make this determination, placing more emphasis on accuracy than efficiency. In this study, a feature engineering pipeline has been created to effectively distinguish between Twitter bots and actual users using user metadata such as default name, description, etc. There has been discussion of several machine learning technique algorithms.An accuracy of 98% using the proposed approach was obtained. The performances of various classifiers like the Decision tree classifier, Random Forest classifier, Multinomial Bayes classifier, KNN, and Logistic Regression classifier are compared to find the best classifier.

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International Journal of Scientific Research in Science and Technology

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Published

2022-05-30

Issue

Volume 9, Issue 3, May-June-2022

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Shweta Patil, Aparna V. Mote "Twitter Data Analysis for BOT Classification" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 3, pp.586-588, May-June-2022.