Understanding Types of Users on Twitter

TL;DR

This paper presents a supervised machine learning method to classify Twitter users into six distinct categories based on profile and behavior features, achieving high accuracy in a cross-validation study.

Contribution

It introduces a comprehensive classification framework for Twitter users, utilizing a broad set of features and demonstrating its effectiveness through empirical evaluation.

Findings

High classification accuracy achieved (AUC, precision, recall)

Effective use of profile and tweeting behavior features

Successful 10-fold cross-validation on 716 profiles

Abstract

People use microblogging platforms like Twitter to involve with other users for a wide range of interests and practices. Twitter profiles run by different types of users such as humans, bots, spammers, businesses and professionals. This research work identifies six broad classes of Twitter users, and employs a supervised machine learning approach which uses a comprehensive set of features to classify users into the identified classes. For this purpose, we exploit users' profile and tweeting behavior information. We evaluate our approach by performing 10-fold cross validation using manually annotated 716 different Twitter profiles. High classification accuracy (measured using AUC, and precision, recall) reveals the significance of the proposed approach.