Examining IRA Bots in the NFL Anthem Protest: Political Agendas and Practices of Digital Gatekeeping
Bibliographic record
Abstract
With the understanding that the mass-participated mechanism of social media has led to an evolved lens of gatekeeping, this study incorporates the framework of digital gatekeeping to examine activities of Internet Research Agency (IRA) bots in the Twitter sphere of the National Football League anthem protest. To do so, the investigation employed data of IRA bots released from Clemson University. We conducted analysis by approaching bots’ gatekeeping activities from three perspectives: the overall behavioral patterns, the discourses and underpinning ideologies, and communicative tactics to sustain attention on Twitter. The results revealed that the majority of tweets came from the right trolls and left trolls. Meanwhile, the activity level of the bots displayed high sensitivity to emergent political events. Importantly, the two types of bots orchestrated a gatekeeping agenda that propelled antagonistic, hyperpartisan politics. The right-wing trolls’ tweets, in particular, propagated pro-White, malicious propaganda infiltrated with fake news. The results yield meaningful implications for digital gatekeeping, social media’s complex roles in knowledge production related to athlete protest, and sport’s engagement in broader political struggles in today’s mediated culture.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".