ESG Communication Tactics and Reputational Capital on Social Media
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
Analyzing 2,309,573 tweets by S&P 500 firms along with 2,498,767 public replies, we examine how firms’ ESG communication tactics on social media influence the micro-level accumulation of reputational capital. Leveraging the prior communication literature, we categorize firms’ ESG messages based on three primary communication functions: Information, Community-Building, and Action. Information-based tactics unidirectionally disseminate knowledge; community-building tactics foster engagement and relationship-building; and action-based tactics seek to mobilize stakeholders to take direct action. Our results indicate that information-focused ESG messages relate to reputational awareness, whereas community-building tactics are associated with reputational favorability. Additional analyses reveal different audience response patterns between ESG-specific and general corporate messaging as well as between B2C and B2B firms. This study provides evidence of new, non-reporting-based ESG communication tactics and illustrates how firms accumulate reputational capital on a micro, message-by-message, day-to-day level. Our findings offer insights into the strategic use of ESG communication to enhance corporate reputation.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.003 |
| 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