Hashtagged Trolling and Emojified Hate against Muslims on Social Media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This empirical exploratory study examines a number of insulting hashtags used against Islam and Christianity on Twitter and Instagram. Using a mixed method, the findings of the study show that Islam is more aggressively attacked than Christianity by three major communities, unlike Christianity, which is targeted much less by two main online groups. The online discussion around the two religions is politically polarized, and the negative language especially used against Islam includes the strategic use of hashtags and emojis, which have been weaponized to communicate violent messages and threats. The study is situated within the discussion of trolling and hateful content on social media. Aside from the empirical examination, the study refers to the differences in Twitter’s and Instagram’s policies, for the latter does not allow using hashtags such as #f***Christians and #f***Muslims, unlike Twitter, which accepts all types of hashtags to be used.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it