Is humour effective in combating hate speech? Maybe not so clearly
Bibliographic record
Abstract
This article presents the main findings of a study focusing on two elements concerning the fight against the circulation of hate speech on the Web: 1) the challenge posed by the need to conceptualize the notion of counter-discourse 2) the experimentation of humour as a strategic tool to combat the circulation of hate speech on the Internet and its negative effects on online social attitudes. As a first step, we conducted eleven semi-structured interviews with six academic researchers and five experts in the field, in order to understand how the common aspects of the counter-discourse concept found in the literature can be interpreted and mobilized in practice. In doing so, we analyzed their responses in order to identify the types of counter-discourse we felt were most effective in combating hate speech, and to determine whether humour was a relevant strategy. Secondly, we conducted an online experiment with two short videos, one conveying counter-speech in a humorous form, the other in a more serious manner. In some respects, the results of the experiment went in the opposite direction to that expected. Indeed, the data show that a message conveyed in a humorous way may be less effective than one presented in a more serious manner. In addition, it seems that variables such as age and perception of the limits of freedom of expression play a significant role in the appreciation and willingness to share this type of material online.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".