Manufacturing rage: The Russian Internet Research Agency’s political astroturfing on social media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines social media ads purchased by the Russian Internet Research Agency (IRA). Using a public dataset, we employ a mixed method to analyze the thematic and strategic patterns of these ads. Due to Facebook and Instagram’s promotional features, IRA managed to microtarget audiences mostly located in the United States fitting its messages to suit the audiences’ political, racial, gendered, and in some cases religious backgrounds. The findings reveal the divisive nature and topics that are dominant in the dataset including: race, immigration, and police brutality. By expanding on the theoretical conceptualization of astroturfing strategy that focuses on carefully concealing the identity and intention of actors behind social media activities, we argue that IRA has added political astroturfing as a powerful tool at a low cost contributing to the broader Russian geopolitical disinformation campaign strategies. The IRA made use of the business model of Facebook and Instagram in an attempt to further divide its targeted audiences and by highlighting mostly negative issues with a potential goal of fuelling political rage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it