A comparative study of social bot classification techniques

With social media rising in popularity over the recent years, new so called social bots are infiltrating by spamming and manipulating people all over the world. Many different methods have been presented to solve this problem with varying success. This study aims to compare some of these methods, on a dataset of Twitter account metadata, to provide helpful information to companies when deciding how to solve this problem. Two machine learning algorithms and a human survey will be compared on the ability to classify accounts. The algorithms used are the supervised algorithm random forest and the unsupervised algorithm k-means. There will also be an evaluation of two ways to run these algorithms, using the machine learning as a service BigML and the python library Scikit-learn. Additionally, what metadata features are most valuable in the supervised and human survey will be compared. Results show that supervised machine learning is the superior technique for social bot identification with an accuracy of almost 99%. To conclude, it depends on the expertise of the company and if a relevant training dataset is available but in most cases supervised machine learning is recommended