  Twitter is one of the most popular social networks attracting millions of
users, while a considerable proportion of online discourse is captured. It
provides a simple usage framework with short messages and an efficient
application programming interface (API) enabling the research community to
study and analyze several aspects of this social network. However, the Twitter
usage simplicity can lead to malicious handling by various bots. The malicious
handling phenomenon expands in online discourse, especially during the
electoral periods, where except the legitimate bots used for dissemination and
communication purposes, the goal is to manipulate the public opinion and the
electorate towards a certain direction, specific ideology, or political party.
This paper focuses on the design of a novel system for identifying Twitter bots
based on labeled Twitter data. To this end, a supervised machine learning (ML)
framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm,
where the hyper-parameters are tuned via cross-validation. Our study also
deploys Shapley Additive Explanations (SHAP) for explaining the ML model
predictions by calculating feature importance, using the game theoretic-based
Shapley values. Experimental evaluation on distinct Twitter datasets
demonstrate the superiority of our approach, in terms of bot detection
accuracy, when compared against a recent state-of-the-art Twitter bot detection
method.
