Addressing backlash? Corporate DEI communication and user complaints on social media
Bibliographic record
Abstract
Companies are using social media to showcase their diversity, equity, and inclusion (DEI) initiatives. However, as DEI is a sensitive and controversial issue, negative reactions in the form of online complaints are common. The purpose of this research was to show how U.S. and German companies communicate about DEI on social media (Facebook and Twitter/X) and how they respond to complaints from their followers. The research is based on theoretical approaches from DEI as well as online complaining and webcare. Data were collected in 2022 employing content analysis. Key findings include that U.S. companies communicated more about DEI than German companies, and that about half of DEI posts received negative reactions, but by no means all of these complaints were about DEI. It shows that both U.S. and German companies very rarely responded to the complaints about DEI and that the response rate was significantly higher when the complaints concerned the company’s products or services. This research suggests that companies need to be prepared to handle complaints in response to their DEI posts on their social media pages and to have sound responses in place, not just for complaints related to their products or services.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 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".