Botivist: Calling Volunteers to Action Using Online Bots
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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.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Sep 2015.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 1; 2024: 0; 2025: 1; 2026: 2.
7 papers.
- 2021CASS: Towards Building a Social-Support Chatbot for Online Health Communitycited by 8
- 2023Demystifying Misconceptions in Social Bots Researchcited by 7
- 2021Artificial Intelligence as an Anti-Corruption Tool (AI-ACT) -- Potentials and Pitfalls for Top-down and Bottom-up Approachescited by 1
- 2021Submodular Optimization Beyond Nonnegativity: Adaptive Seed Selection in Incentivized Social Advertisingcited by 1
- 2025Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocationscited by 1
- 2026Evaluating Large Language Models on Misconceptions in Multi-Turn Medical Conversationscited by 0
- 2026How Formerly Incarcerated People Envision Technologies for Prison Parolecited by 0
- 2026How Formerly Incarcerated People Envision Technologies for Prison Parole17 Jul 2026
- 2026Evaluating Large Language Models on Misconceptions in Multi-Turn Medical Conversations14 Jul 2026
- 2025Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocations29 Sep 2025
- 2023Demystifying Misconceptions in Social Bots Research30 Mar 2023
- 2021Submodular Optimization Beyond Nonnegativity: Adaptive Seed Selection in Incentivized Social Advertising30 Sep 2021
- 2021Artificial Intelligence as an Anti-Corruption Tool (AI-ACT) -- Potentials and Pitfalls for Top-down and Bottom-up Approaches23 Feb 2021
- 2021CASS: Towards Building a Social-Support Chatbot for Online Health Community4 Jan 2021