Corporate social responsibility and public diplomacy as formulas to reduce hate speech on social media in the fake news era
Bibliographic record
Abstract
Purpose Analyse the presence of hate speech in society, placing special emphasis on social media. In this sense, the authors strive to build a formula to moderate this type of content, in which platforms and public institutions cooperate, from the fields of corporate social responsibility and public diplomacy, respectively. Design/methodology/approach To this aim, it is important to focus efforts on the creation of counter-narratives; the establishment of content moderation guidelines, which are not necessarily imposed by unilateral legislation; the promotion of suitable scenarios for the involvement of civil society; transparency on the part of social media companies; and supranational cooperation that is as transnational as possible. To exemplify the implementation of initiatives against hate speech, two cases are analysed that are paradigmatic for assuming two effective approaches to the formula indicated by the authors. Findings The authors analyse, in the case of the European Union, its “Code of conduct to counteract illegal online hate speech”, which included the involvement of different social media companies. And in the case of Canada, the authors discuss the implementation of the bill to include a definition of hate speech and the establishment of specific sanctions for this in the Canadian Human Rights Act and the Canadian Penal Code. Originality/value The case of the European Union was a way of seeking consensus with social media companies without legislation, while the case of Canada involved greater legislative and penalisation. Two ways of seeking the same goal: curbing hate speech.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".