ESG disclosures as a double-edged sword: Protective impacts and risks in the social media era
Bibliographic record
Abstract
Social media has permeated everyday life, providing platforms for information dissemination, personal sharing, and maintaining interpersonal connections. Its role in the business domain has become equally pivotal, serving as a vital conduit for corporate communication, consumer engagement, and community interaction. Crucially, social media levels the communicative playing field, empowering stakeholders previously devoid of a voice to amplify their concerns and rally like-minded individuals, potentially exerting substantial influence on corporate entities. This study explores the influence of Weibo discussions on Environmental, Social, and Governance (ESG) topics on the stock performance of 141 Chinese companies listed in Hong Kong, utilizing 4,320 hot search topics to analyze market reactions. Our findings corroborate the significant role of social media as a digital citizen square where stakeholders not traditionally engaged in financial dialogues can significantly impact market perceptions and valuations. We find that negative discussions markedly harm company valuations, while positive sentiments foster minor beneficial effects on stock prices. Crucially, our research highlights the complex functionality of ESG disclosures as both a shield and a potential risk: while they can mitigate negative repercussions during crises of negative publicity, they also set high transparency standards that may increase scrutiny and potentially lead to adverse market outcomes during stable periods. This study extends the signaling theory to social media contexts and provides empirical support for ESG's role in risk mitigation, offering nuanced insights into stakeholder theory and ESG communication strategies in the digital age.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".