Development of Bot Detection Applications on Twitter Social Media Using Machine Learning with a Random Forest Classifier Algorithm

Abstract

A Twitter bot is a Twitter account programmed to automatically do social activities by sending tweets through a scheduling program. Some bots intend to disseminate useful information such as earthquake and weather information. However, not a few bots have a negative influence, such as broadcasting false news, spam, or become a follower to increase an account's popularity. It can change public sentiments about an issue, decrease user confidence, or even change the social order. Therefore, an application is needed to distinguish between a bot and non-bot accounts. Based on these problems, this paper develops bot detection systems using machine learning for multiclass classification. These classes include human classes, informative, spammers, and fake followers. The model training used guided methods based on labeled training data. First, a dataset of 2,333 accounts was pre-processed to obtain 28 feature sets for classification. This feature set came from analysis of user profiles, temporal analysis, and analysis of tweets with numeric values. Afterward, the data was partitioned, normalized with scaling, and a random forest classifier algorithm was implemented on the data. After that, the features were reselected into 17 feature sets to obtain the highest accuracy achieved by the model. In the evaluation stage, bot detection models generated an accuracy of 96.79%, 97% precision, 96% recall, and an f-1 score of 96%. Therefore, the detection model was classified as having high accuracy. The bot detection model that had been completed was then implemented on the website and deployed to the cloud. In the end, this machine learning-based web application could be accessed and used by the public to detect Twitter bots.