An in-depth characterisation of Bots and Humans on Twitter

TL;DR

This study compares the behaviors and impacts of bots and humans on Twitter using large-scale data, metrics, and annotations to identify key differences and similarities for improved bot detection.

Contribution

It provides a comprehensive analysis of bot and human activity on Twitter across popularity groups, introducing new metrics and validation methods for bot classification.

Findings

Identifies behavioral differences between bots and humans.

Reveals surprising similarities across popularity groups.

Supports development of more reliable bot detection methods.

Abstract

Recent research has shown a substantial active presence of bots in online social networks (OSNs). In this paper we utilise our past work on studying bots (Stweeler) to comparatively analyse the usage and impact of bots and humans on Twitter, one of the largest OSNs in the world. We collect a large-scale Twitter dataset and define various metrics based on tweet metadata. We divide and filter the dataset in four popularity groups in terms of number of followers. Using a human annotation task we assign 'bot' and 'human' ground-truth labels to the dataset, and compare the annotations against an online bot detection tool for evaluation. We then ask a series of questions to discern important behavioural bot and human characteristics using metrics within and among four popularity groups. From the comparative analysis we draw important differences as well as surprising similarities between the two entities, thus paving the way for reliable classification of automated political infiltration, advertisement campaigns, and general bot detection.