Examining the Social Media Addiction Levels of Young Adults: Turkey Example
Bibliographic record
Abstract
Social media has often started to be used as the first source for accessing information. Country agenda, general issues, research, new ideas, entertainment, shopping, instant communication and cooperation are the positive contributions of social media. Excessive use of social media in every aspect of life certainly causes harm. It causes serious psychological deformation, especially on adolescents, whom we call the young group. While social media can increase technical skills in adolescents, it can reduce social skills and communication and even some of the negative effects they encounter in this area can create psychological trauma. While the feeling of inadequacy, comparison and competition lead to psychological problems such as self-confidence and social anxiety; the virtualized perception of beauty poses dangers such as eating disorders in young people. Social media use, which has reached the point of addiction, adds a number of negativities to young people’s lives, such as sleep deprivation, decreased time spent with family and loss of interest in real life. In light of all this, the aim of this research is to examining the social media addiction levels of young adults. The sample of the study consists of 201 university students studying in different departments of a state university in Turkey and whose age range is between 18-30 and more age. “Social media addiction scale - adult form” scale developed by Sahin and Yagcı (2017) was used as a data collection tool in the study, with permission from the developer authors. In the study, the relational screening method, one of the quantitative research methods, was used. As a result, a significant relationship was detected between the variables of young people’s gender, age, daily internet and social media usage and their social media addictions. In addition the average social media addiction scores of university students were found to be high.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".