Exploring Parents' Motivations for Sharenting and Consequences for Children's Well-Being
Bibliographic record
Abstract
Introduction: This literature review uses thematic analysis to identify common themes and topics in recent literature on the perceptions, attitudes, and motivations towards parents’ sharenting behavior. Objective: This study investigates perspectives on sharenting—the practice of parents posting content about their children on social media—and the rationale behind this behavior. As digital platforms become more integrated into family life, understanding the motives for sharing is critical for assessing their social, ethical, and developmental consequences. Methods: Articles were selected through a literature search. We eliminated articles that included sharenting, sharenting practices in Malaysia, impression management and sharenting, and reinforcement theory and sharenting. 41 articles were chosen and reviewed to identify the main topics of discussion. Findings: This study identifies major motives for sharing, as revealed through qualitative interviews and surveys with parents and social media users, including a need for social connection, community support, and documenting parenting milestones. The findings reflect a variety of viewpoints on the practice, with some seeing sharing as a way to celebrate parenting and develop relationships. In contrast, others are concerned about privacy and the digital legacy left for children. Conclusion: By analyzing these perspectives, the study contributes to the broader discussion of digital parenting practices and sheds light on the balance between sharing and privacy in the digital era. This study emphasizes the importance of raising parental awareness and providing help as they navigate the difficulties of social media sharing. Recommendation: These results serve as a reference for future child psychology and mental health research. Thus, it is recommended that parental sharenting behavior be further explored, and a suitable legal framework should be established in Malaysia to govern and manage this issue before violations related to sharenting, such as digital kidnapping and cyberbullying, become difficult to address in the Malaysian context.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".