# Measuring bot and human behavioral dynamics

arXiv: https://arxiv.org/abs/1802.04286 · HTML: https://mlsanity.com/paper/1802.04286/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2018.

## Built on by (most cited first, 7 of 7)

- [Online Detection of LLM-Generated Texts via Sequential Hypothesis Testing by Betting](https://mlsanity.com/paper/2410.22318/) (2024)
- [IOHunter: Graph Foundation Model to Uncover Online Information Operations](https://mlsanity.com/paper/2412.14663/) (2024)
- [BotHawk: An Approach for Bots Detection in Open Source Software Projects](https://mlsanity.com/paper/2307.13386/) (2023)
- [BotSpot: Deep Learning Classification of Bot Accounts within Twitter](https://mlsanity.com/paper/2109.03710/) (2021)
- [Bots Don't Sit Still: A Longitudinal Study of Bot Behaviour Change, Temporal Drift, and Feature-Structure Evolution](https://mlsanity.com/paper/2512.17067/) (2025)
- [Characterising Behavioural Families and Dynamics of Promotional Twitter Bots via Sequence-Based Modelling](https://mlsanity.com/paper/2512.18077/) (2025)
- [Manufactured Divisiveness: Decomposing the Hostile Content of Seven Social Media Influence Operations](https://mlsanity.com/paper/2607.14491/) (2026)

## Abstract (arXiv)

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.