Social mediatization of religion: islamic videos on YouTube
Bibliographic record
Abstract
Based on the conceptual framework of social mediatization of religion, this paper seeks to understand three pertinent phenomena: trends of online religious content, users' engagement with them, and correlations between interaction indicators. To answer the research inquiries, we quantitatively analyzed 73,120 Islamic videos uploaded on YouTube in 2011-2020. The result shows that Islamic videos on YouTube are growing continuously without any decline, from 6.04% in 2011 to 13.11% in 2019, more than two-fold in eight years. Also, with a strong positive correlation, comments and likes (r = .862; p < .01) are increasing at a remarkable rate than the other interaction variables. We observed that users who watch Islamic videos are more likely to like the videos than to dislike them. Users' engagement is not related to the length of Islamic videos in any significant way. These two results partly suggest users' positive and supportive attitudes toward online Islamic videos. Finally, this study recommends investigating the thematic temporal distribution of Islamic videos for an in-depth understanding of the users' topical interest patterns.
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".