Understanding Characteristics of Catalyst Users in the WallStreetBets Community
Bibliographic record
Abstract
WallStreetBets (WSB), a Reddit community, impacted stock markets during the 2021 GameStop Short Squeeze. We examine the content and user properties that influence engagement in WSB. Despite WSB's association with emojis and informal terms, engagement among community members depends on more than surface-level factors. Although emojis are commonly used, they are not as effective at fostering interactions among users. Community members engage more with posts that have longer and topic-specific text. Simply producing a high volume of posts is not enough to attract an audience. Consistent topical focus, reciprocal interactions, and previous authorship of catalyst posts influence engagement. WSB posts, regardless of length, generally remain relevant to the community's theme of stock trading. Our findings provide insights into WSB engagement patterns and can be useful for downstream research, such as financial predictive tasks using WSB data.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".