Social Media and Online Digital Technology Use Among Muslim Young People and Parents: Qualitative Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Digital technology and social media use are common among young people in Australia and worldwide. Research suggests that young people have both positive and negative experiences online, but we know little about the experiences of Muslim communities. OBJECTIVE: This study aims to explore the positive and negative experiences of digital technology and social media use among young people and parents from Muslim backgrounds in Melbourne, Victoria, Australia. METHODS: This study involved a partnership between researchers and a not-for-profit organization that work with culturally and linguistically diverse communities. We adopted a participatory and qualitative approach and designed the research in consultation with young people from Muslim backgrounds. Data were collected through in-person and online focus groups with 33 young people aged 16-22 years and 15 parents aged 40-57 years. Data were thematically analyzed. RESULTS: We generated 3 themes: (1) maintaining local and global connections, (2) a paradoxical space: identity, belonging and discrimination, and (3) the digital divide between young Muslims and parents. Results highlighted that social media was an important extension of social and cultural connections, particularly during COVID-19, when people were unable to connect through school or places of worship. Young participants perceived social media as a space where they could establish their identity and feel a sense of belonging. However, participants were also at risk of being exposed to discrimination and unrealistic standards of beauty and success. Although parents and young people shared some similar concerns, there was a large digital divide in online experiences. Both groups implemented strategies to reduce social media use, with young people believing that having short technology-free breaks during prayer and quality family time was beneficial for their mental well-being. CONCLUSIONS: Programs that address technology-related harms must acknowledge the benefits of social media for young Muslims across identity, belonging, representation, and social connection. Further research is required to understand how parents and young people can create environments that foster technology-free breaks to support mental well-being.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".