Detection of Bot Accounts on Social Media Considering Its Imbalanced Nature

Abstract

Social media has completely transformed the way people communicate. However, every revolution brings with it some negative impacts. Due to its popularity amongst tons of global users, these platforms have a huge volume of data. The ease of access with minimal verification of new users on social media has led to the creation of the bot accounts used to collect private data, spread false and harmful content, and also poses many security threats. A lot of concerns have been raised with the increment in the quantity of bot accounts on different social media platforms. Also there is a high imbalance between bot and non-bot accounts where the imbalance is a result of 'normal behavior' of bot users. The research aims at identifying the artificial bots accounts on Twitter using various machine learning algorithms and content-based classification based on features provided on the platform and recent tweets of users respectively.

Detection of Unnatural User Behavior in Social Media Using Machine Learning Technologies

“…The program is written in the Java programming language with the connection of the Selenium library for automating the testing of web pages in browsers [1,2]. For the collected data from several social networks, it is possible to summarize and analyze the data for the presence of bots according to certain attributes [3]. In addition, it is worth taking into account a larger number of parameters for training and classification.…”