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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".