The Influence of Alexithymia on the Tendency of Social Media Addiction in Early Adults in Karawang
Bibliographic record
Abstract
The internet offers numerous features that simplify activities like communication, information retrieval, and transactions, making social media increasingly accessible. In Indonesia, individuals in their productive years, particularly early adulthood, dominate internet usage. This phase is characterized by repetitive behaviors, which can escalate the intensity of social media use and potentially lead to addiction. One factor influencing this tendency is alexithymia. This study aims to explore the impact of alexithymia on social media addiction tendencies among early adults in Karawang. Employing a quantitative causality design, the research utilized non-probability sampling, specifically snowball sampling, to gather data from 385 respondents aged 20-30 years residing in Karawang. The Toronto Alexithymia Scale (TAS) was used to measure alexithymia, while the Bergen Social Media Addiction Scale (BSMAS) assessed social media addiction tendencies. Reliability was tested using Cronbach's Alpha, with a threshold of > 0.08, while normality was evaluated through the Kolmogorov-Smirnov test at a 5% significance level. Linearity was assessed using an ANOVA test, yielding a significance of 0.468 > 0.05. The results of the simple linear regression test showed a significance value of 0.000 < 0.05, confirming the hypothesis that alexithymia significantly influences social media addiction tendencies in early adulthood in Karawang. The determination test results indicated that alexithymia accounts for 54.2% of the variance in social media addiction tendencies (R-Square = 0.542), with the remaining 45.8% attributed to other variables not examined in this study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".