Social Media and Voluntary Nonfinancial Disclosure: Evidence from Twitter Presence and Corporate Political Disclosure
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.
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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".