  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.
