Factors leading to being unfriended on Facebook among adults.
Bibliographic record
Abstract
The social media is a popular mean to connects with friends and families. Studies shown that it provides social and emotional support to those connected, apart from acting as an information resources such as news, products, and services. This paper is interested to investigate the users’ behaviour and aim to study the factors leading to being unfriended on Facebook among adults. Social networking sites such as Facebook are chosen as adults widely use them. This study used Expectancy Violations Theory (EVT) as foundations theories for humans that create comfort or discomfort to one another. The minimum sample was collected using Google Form. The study involved 110 respondents aged 20 to 31 and above from men and women in Malaysia. A cross-sectional questionnaire was used for data collection. All variables were measured through data sum from the Google Form Gantt chart. The data stating a few factors that might lead to reasons being unfriended on Facebook. The results show that 54.5% of them agreed that the main reason for being unfriended on Facebook is due to people’s personalities. Almost 75.5% of the respondents claimed to have ever experienced being unfriended, and 68.2% of it came from strangers. However, strangers or not, it is widely considered that being unfriended triggers an act of expectancy violations that could vary in terms of situations and the reasons behind it. Thus, studies suggest that being unfriended is negative and creates discomfort among the parties.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".