Verifiable content in social media stock‐analysis articles: The long and short of it
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
Abstract Investment‐related social media platforms are transforming the traditional intermediary landscape by rapidly disseminating user‐empowered opinions and recommendations, raising concerns about the credibility of information on such platforms. We examine the differential content verifiability of SeekingAlpha.com articles with sell recommendations (short articles) versus articles with buy recommendations (long articles). We find that short articles contain greater verifiable content than long articles, and verifiable content in short articles generates greater market reactions and better mitigates return reversals than that in long articles. This asymmetry contrasts prior research evidence that greater content verifiability accompanies traditional sell‐side analyst reports with buy recommendations. Our results are robust to various confounding factors, including author effects, among others. Our results become more pronounced in the presence of greater retail ownership. Taken together, our results provide new evidence on investors’ assessment of the credibility of information on social media platforms.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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 it