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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".