ESG Communication Tactics and Reputational Capital on Social Media
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.
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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".