From Birthday Cheers to Privacy Fears: Unraveling the Paradox of Social Media Celebrations in Nigeria
Bibliographic record
Abstract
Social media users normally congratulate each other with goodwill messages on their birthdays. This can unintentionally reveal personal information and possibly compromise privacy, leading to potential risks. This research examines birthday disclosure behaviors on social media across northern and southern Nigeria. Through a detailed analysis of 700 participants, the study investigates the impact of regional distinctions on social media privacy practices. Despite initial hypotheses of significant regional differences, our analysis, which employed descriptive and structural equation model (SEM) methodologies, revealed a surprising similarity in perceptions and behaviors toward social media disclosures across both regions. Social gratification was a major influence on disclosing birthday information, irrespective of the region. The thematic analysis suggests that Northerners would consider if disclosing their birthday information would increase their reputation, while Southerners consider receiving gifts, wishes, and celebrations on their birthdays. Unlike in the South, some sections of the North also noted religious reasons as a concern for not disclosing their birthday. This study contributes to the global dialogue on digital privacy but also suggests a shared digital culture within Nigeria, providing insights for developing culturally sensitive privacy policies and practices.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".