Sustainability tweeting triumphs during the COP events: analysing environmental, social and governance (ESG) communication on twitter
Bibliographic record
Abstract
Purpose With the recent conclusion of the United Nations Conference of the Parties (COP) 28 in the United Arab Emirates, this study aims to investigate the tweeting behaviour of firms surrounding COP events. The authors analyse the environmental, social and governance (ESG) tweets from the COP 26 and COP 27 events, aiming to deepen the understanding of the complex relationships between social media communication, industry characteristics and financial performance. This timely analysis is critical for assessing how the latest global discussions on climate change are influencing corporate communication strategies on sustainability, offering fresh insights into the evolving dynamics of ESG engagement in the context of these pivotal international meetings. Design/methodology/approach In this study, the authors embrace a grounded theory approach to gain insights into the ESG and sustainability initiatives presented by companies on social media, with an intensified focus on climate change discourse. Leveraging advanced social media analytics, this study expands its scope by conducting a thorough examination of ESG-related tweets from Standard and Poor’s (S&P) 500 companies. In addition, the authors explore the relationships between such communication efforts and financial performance, applying an advanced cumulative abnormal returns (CARs) model. This methodological enhancement enables a more sophisticated understanding of how ESG communication on Twitter correlates with, and potentially influences, a firm’s market valuation and financial health, offering invaluable insights into the strategic importance of digital sustainability discourse. Findings The research findings introduce four novel distinct groups – Unengaged, Catalysts, Cautious and Shapers – based on firms’ proactive or reactive sustainability communication patterns. The results explore the potential impact of COP event locations on tweeting behaviour, proposing that conferences held in different regions, such as Asia versus Europe, may elicit varied reactions from S&P 500 firms. Despite no significant inter-industry differences in tweeting habits, the authors discover a significant link between firms’ financial metrics, specifically CARs, and their categorised communication styles. The results challenge the simplistic view that higher social media engagement leads to positive financial outcomes, suggesting instead that lower financial performance may drive firms to adopt more extreme communication patterns, possibly as a strategic move to enhance corporate legitimacy. Originality/value This study offers new insights into how companies use social media during significant climate change events, namely, COP events. By classifying firms according to their ESG communication approaches, the results reveal uncharted correlations between how companies communicate on social media, namely, Twitter, and the correlation to financial performance.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".