Unpacking the Social Media Bot: A Typology to Guide Research and Policy
Open accessAbstract
Amid widespread reports of digital influence operations during major elections, policymakers, scholars, and journalists have become increasingly interested in the political impact of social media bots. Most recently, platform companies like Facebook and Twitter have been summoned to testify about bots as part of investigations into digitally enabled foreign manipulation during the 2016 U.S. Presidential election. Facing mounting pressure from both the public and from legislators, these companies have been instructed to crack down on apparently malicious bot accounts. But as this article demonstrates, since the earliest writings on bots in the 1990s, there has been substantial confusion as to exactly what a “bot” is and what it does. We argue that multiple forms of ambiguity are responsible for much of the complexity underlying contemporary bot‐related policy, and that before successful policy interventions can be formulated, a more comprehensive understanding of bots—especially how they are defined and measured—will be needed. In this article, we provide a typology of different types of bots, provide clear guidelines for better categorizing political automation, and unpack the impact that it can have on contemporary technology policy. We conclude by outlining the main challenges and ambiguities that will face both researchers and legislators as they tackle bots in the future.
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Bots are being deployed at large scale for political interference and influence.
“the general trend of deploying bots in large scale for political interference and influence [44]”
From Future directions for chatbot research: An interdisciplinary research agenda (2021) · cites this paper as [44]
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- Authors
- Gorwa, R., & Guilbeault, D.
- Affiliations
- University of Oxford (GB)
- University of Pennsylvania (US)
- Venue
- Policy & Internet
- Publisher
- Wiley
- Published
- 2018-08-10
- Volume
- 12
- Issue
- 2
- Pages
- 225-248
- ISSN
- 1944-2866 (ISSN-L)
- OpenAlex
- W2953122191
- Cited by
- 270
- References
- 75
- Citations by year
- 2026: 15 · 2025: 31 · 2024: 37 · 2023: 33 · 2022: 61 · 2021: 33 · 2020: 43 · 2019: 15 · 2018: 2
- FWCI
- 52.83
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Policy & Internet (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Social Sciences and Humanities Research Council of Canada
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- social bots, election interference, 2016 US presidential election, technology policy, platform companies, Facebook, Twitter
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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