Social Media and Voluntary Nonfinancial Disclosure: Evidence from Twitter Presence and Corporate Political Disclosure
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT This study uses a sample of 1,316 firm-year observations of S&P 500 companies (2012–2016) to investigate whether and how social media (i.e., Twitter) affects firms' voluntary nonfinancial disclosure (i.e., corporate political disclosure). Our results show that Twitter-adopting firms are generally more transparent in their disclosure of corporate political contributions and of related policies and board oversight. Moreover, firms with more Twitter followers and firms whose corporate political activities are targeted in more Twitter messages are more transparent in such disclosures. Our cross-sectional analysis suggests that this effect is stronger for firms whose stakeholders are more active on Twitter and firms that are less visible or more reputable. Our results remain robust to different econometric model specifications and controlling for alternative social media platforms. Taken together, our findings suggest that social media (i.e., Twitter) presence exerts pressure on firms' voluntary nonfinancial disclosure practices (i.e., corporate political disclosure). JEL Classifications: G38; M41; M48. Data Availability: Data are available from the sources indicated in the text.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.006 |
| 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