FOMO-Centricity: How Social Media's Dark Designs Cause Users to Reluctantly Give Up their Data
Bibliographic record
Abstract
This thesis explores the link between the Fear of Missing Out (FoMO) and reluctant privacy-compromising behaviours on social media.We first conducted a literature review which laid the groundwork for FoMO as a possible explanation for a gap between users' privacy attitudes and behaviours.To better understand this phenomenon, we used Grounded Theory to conduct and analyze semi-structured interviews with 25 participants.We found strong evidence that participants experience ambivalence in their participation behaviours and feel pressured to participate even when they have privacy concerns to avoid missing out.We present an empirically-based high level theory describing the cyclical relationship between FoMO-centric design and privacyrelated participatory reluctance, and identify three main participatory dimensions.First and foremost, thank you to my supervisor, Dr. Sonia Chiasson, for being my steadfast guide throughout my Master's studies.You have consistently helped to elevate my ideas and have helped me to become a better researcher and a more concise and eloquent writer.Thank you for your patience, encouragement, and for being a pillar of strength even amongst a global pandemic!
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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 | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".