Measuring bot and human behavioral dynamics

TL;DR

This paper analyzes behavioral patterns of bots and humans on Twitter during activity sessions, revealing short-term human-specific trends and developing a machine learning method for improved bot detection.

Contribution

It introduces a novel analysis of behavioral dynamics over activity sessions and proposes a machine learning approach to distinguish bots from humans based on these behaviors.

Findings

Humans exhibit short-term behavioral trends linked to cognitive processes.

Bots lack these short-term behavioral signatures due to automation.

A machine learning algorithm effectively differentiates bot and human activity sessions.

Abstract

Bots, social media accounts controlled by software rather than by humans, have recently been under the spotlight for their association with various forms of online manipulation. To date, much work has focused on social bot detection, but little attention has been devoted to the characterization and measurement of the behavior and activity of bots, as opposed to humans'. Over the course of the years, bots have become more sophisticated, and capable to reflect some short-term behavior, emulating that of human users. The goal of this paper is to study the behavioral dynamics that bots exhibit over the course of one activity session, and highlight if and how these differ from human activity signatures. By using a large Twitter dataset associated with recent political events, we first separate bots and humans, then isolate their activity sessions. We compile a list of quantities to be measured, like the propensity of users to engage in social interactions or to produce content. Our analysis highlights the presence of short-term behavioral trends in humans, which can be associated with a cognitive origin, that are absent in bots, intuitively due to their automated activity. These findings are finally codified to create and evaluate a machine learning algorithm to detect activity sessions produced by bots and humans, to allow for more nuanced bot detection strategies.