Unmasking Twitter Bots: Feature Engineering and Machine Learning for Bot Account Identification

Abstract

Bot accounts on X (formerly known Twitter)1 are a growing issue that limits and negatively impacts the browsing and sharing experience of Twitter users, and it is important to identify such accounts. In this work, we perform machine learning-based estimation of bot accounts on Twitter. Using Twitter's API, a data set is collected containing tweets and related metadata from various accounts. Feature engineering techniques are then applied to highlight relevant features such as sentiment analysis of the account's tweets, or the account's friend/follower ratio. Using these features to train and evaluate machine learning models, the likelihood of a given account being a bot is estimated. The performances of three different models are comparatively analyzed based on their fl score, accuracy, precision, and recall. Analysis of feature importances shows the success of derived features in identifying bot accounts. This work demonstrates the potential of using feature engineering with tweet and profile properties to detect bot accounts on Twitter, and provides a foundation for further research on this topic.1In October 2022, Twitter was acquired, after which Twitter Inc. ceased to exist as an independent company and was merged with X Corp. To prevent possible inconsistencies and confusion in literature search results, we used the name Twitter throughout the text. © 2023 Elsevier B.V., All rights reserved.