Episodes of sustained protest: temporal patterns of online mobilization on X
Bibliographic record
Abstract
The rise of social media has allowed the rapid manifestation of collective actions that are both large in scale and persist over extended periods of time. Yet, little is known about how the mobilization strategies within protests vary over time. By drawing on literature on online mobilization patterns, the concept of cycles of contention and Resource Mobilization Theory, we develop and test a theoretical framework of the development of protest collectives’ communication strategies during episodes of sustained protest. We employ the large language model (LLM) GPT-4 Turbo to analyze a sample of 8,583 posts published on Twitter (now X) by supporters of the 2022 Freedom Convoy in Canada. Our findings show that political mobilization strategies are prevalent at times when there is potential to motivate large numbers of people to join the protest, with coordination efforts being used to facilitate logistics during these times. Conversely, as the protest progresses, shared information and discussions become increasingly marked by anger and fear, which may reflect efforts to sustain participation of a shrinking core of committed supporters. Beyond these theoretical contributions, our study adds to the growing literature demonstrating that LLMs can produce accurate classifications for text as data, even when tasks are complex.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".