Shaping collective action in financial markets through popular expertise: An analysis of Due Diligence posts on WallStreetBets
Bibliographic record
Abstract
In 2021, a social movement rallying retail investors unexpectedly shocked Wall Street, forcing a prominent multi-billion-dollar hedge fund to shut down one year later, after incurring massive financial losses. Social movements in financial markets have significantly developed in the wake of the 2007–09 financial crisis, resulting in the emergence of various collective actions. We analyze one recent example of such action undertaken by the r/WallStreetBets (WSB) community on Reddit, which disrupted the stock prices of several “meme stocks” (e.g., GameStop) by disseminating influential investment narratives. We analyze the 150 most upvoted Due Diligence posts on WSB and interview eight members of its community. We find that a popular expertise in investment narratives emerged, developed, and was propagated on this digital platform. WSB authors' claim to popular expertise is made in a hybrid language combining traditional financial expertise with an accessible and entertaining writing style, complemented by references to pop culture. Our analysis brings out a growing resentment among retail investors about the unfairness of financial markets, and its role in mobilizing them for collective action that challenged the existing order of things. Yet this widespread resentment did not spontaneously translate into a meaningful, sustainable collective action initiative. Our thesis is that the development of popular expertise played an instrumental role in the formation of WSB's collective action initiative targeting several perceived investment opportunities.
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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 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".