# Botivist: Calling Volunteers to Action Using Online Bots

arXiv: https://arxiv.org/abs/1509.06026 · HTML: https://mlsanity.com/paper/1509.06026/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2015.

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

- [CASS: Towards Building a Social-Support Chatbot for Online Health Community](https://mlsanity.com/paper/2101.01583/) (2021)
- [Demystifying Misconceptions in Social Bots Research](https://mlsanity.com/paper/2303.17251/) (2023)
- [Artificial Intelligence as an Anti-Corruption Tool (AI-ACT) -- Potentials and Pitfalls for Top-down and Bottom-up Approaches](https://mlsanity.com/paper/2102.11567/) (2021)
- [Submodular Optimization Beyond Nonnegativity: Adaptive Seed Selection in Incentivized Social Advertising](https://mlsanity.com/paper/2109.15180/) (2021)
- [Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocations](https://mlsanity.com/paper/2509.25492/) (2025)
- [Evaluating Large Language Models on Misconceptions in Multi-Turn Medical Conversations](https://mlsanity.com/paper/2607.12884/) (2026)
- [How Formerly Incarcerated People Envision Technologies for Prison Parole](https://mlsanity.com/paper/2607.16513/) (2026)

## Abstract (arXiv)

To help activists call new volunteers to action, we present Botivist: a platform that uses Twitter bots to find potential volunteers and request contributions. By leveraging different Twitter accounts, Botivist employs different strategies to encourage participation. We explore how people respond to bots calling them to action using a test case about corruption in Latin America. Our results show that the majority of volunteers (>80%) who responded to Botivist's calls to action contributed relevant proposals to address the assigned social problem. Different strategies produced differences in the quantity and relevance of contributions. Some strategies that work well offline and face-to-face appeared to hinder people's participation when used by an online bot. We analyze user behavior in response to being approached by bots with an activist purpose. We also provide strong evidence for the value of this type of civic media, and derive design implications.