Disruptive identity politics around Black Lives Matter and Blue/All Lives Matter
Bibliographic record
Abstract
In this study, we present a cross-national comparative analysis of online trolling by focusing on Russian and Iranian trolls who discussed the Black Lives Matter (BLM) and Blue/All Lives Matter (B/ALM) movements with what we term disruptive identity politics . Leveraging identity politics, they impersonated US nationals to legitimize their disinformation activities. Using novel digital tools to extract the data and employing manual multimodal analyses, we found that the degree of orientation or opposition that the Russian and Iranian trolls had toward either BLM or B/ALM mirrored geopolitical realities. Iranian trolls showed more support for BLM (63% of tweets) than for B/ALM (14.6%). The reverse was the case with the Russian trolls, who were more supportive of B/ALM (78.7%) than of BLM (73.5%). The Iranian trolls also had very little opposition to BLM, with only 1% of BLM and B/ALM samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".