Social Media Preferences, Interrelations Between the Social Media Characteristics and Culture: A View of Arab Nations
Bibliographic record
Abstract
This article is a pioneering theoretical work as an approach from the perspective of combination of social media characteristics and wisdom of crowds. The aim of this article is to conceptualize the social media channels in terms of their characteristics and to discuss the correlation between social media choices and culture. This paper contributes to social media marketing theory by developing a conceptual approach that explains and offers implications pertaining to the relationship between different social media channels in the age of social media. To do this, the relevant literature has been thoroughly reviewed, and exploratory research method has been used. The study also aims to visualize the effect of cultural differences as aligned with the study of Edward Hall (1967) and Gert Hoftstede (1984). Cultural factors exert a broad and deep influence on social media choices. Arab countries are categorized as high-context culture according to Hall's dimensions. The social media reports, Arab Social Media, conducted by Dubai School of Government of the years from 2013, 2013 and 2017 were analyzed. The limited availability of the literature done on the region has limited the scope and analysis of the research. The paper concludes that social media has certain characteristics that interrelate with each other and culture has a moderation effect on the choices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".