Addictive Facebook Use among University Students
Bibliographic record
Abstract
The Facebook has become an essential part of almost every university students’ daily life, and while a large number of students seem to get benefits from use of the Facebook by exchanging information for educational goals, make friends, and other activities, the literature indicates that this social networking site can become addictive to some university students’ users, which is one of the today’s higher education matters. The aim of this study, therefore, is to explore the phenomenon of Facebook addiction among university students. Qualitative study using interview is used to gather data from nine International postgraduates of Universiti Putra Malaysia and the data established three themes (Compulsion to check Facebook, High frequency use, and Using Facebook to avoid offline responsibility) relied on the participants interviews. The findings from these three themes showed that these users considered their Facebook dependency, are known as salience, tolerance, and conflict. These results also lead to the conclusion that like most activities, moderation and controlled use are key. So, the best approach to preparing students for life in a knowledge-based society is to help them exercise self-control and achieve a level of balance when using Facebook. It is believed that the findings of this study would help other Facebook researchers by contributing to the limited academic literature in this area.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".