Unpacking the Social Media Bot: A Typology to Guide Research and Policy
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.013 | 0.056 |
| Scholarly communication | 0.020 | 0.055 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".