Bibliographic record
Abstract
While social media offer users a platform for self-expression, identity exploration, and community management, among other functions, they also offer space for religious practice and expression. In this paper, we explore social media spaces as they subtend new forms of religious experiences and rituals. We present a mixed-method study to understand the practice of sharing Quran verses on Arabic Twitter in their cultural context by combining a quantitative analysis of the most shared Quran verses, the topics covered by these verses, and the modalities of sharing, with a qualitative study of users' goals. This analysis of a set of 2.6 million tweets containing Quran verses demonstrates that online religious expression in the form of sharing Quran verses both extends offline religious life and supports new forms of religious expression including goals such as doing good deeds, giving charity, holding memorials, and showing solidarity. By analysing the responses on a survey, we found that our Arab Muslim respondents conceptualize social media platforms as everlasting, at least beyond their lifetimes, where they consider them to be effective for certain religious practices, such as reciting Quran, supplication (dua), and ceaseless charity. Our quantitative analysis of the most shared verses of the Quran underlines this commitment to religious expression as an act of worship, highlighting topics such as the hereafter, God's mercy, and sharia law. We note that verses on topics such as jihad are shared much less often, contradicting some media representation of Muslim social media use and practice.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.255 | 0.182 |
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".