Folk classification of social media platforms: Preliminary findings
Bibliographic record
Abstract
There is little understanding of how and on what grounds people perceive or group Social Media Platforms (services) in which they participate into categories that can explain or predict their patterns of use. Some models are discussed and the possibilities of an ecological model are highlighted. This study examined how active users grouped social media platforms. 59 respondents completed an open card sort activity where they categorized 19 social media applications according to their own preferences. Data was also collected on frequency of use of Social Media Platforms as well as perceived use in comparison with peers. Using a series of decision rules, 44 standardized categories were defined. A similarity matrix and dendogram are presented that show strong and weak associations between platforms. A post-session survey provided an opportunity for participants to comment on their organizational preferences and from this user-perceived were themes identified. A discourse analysis of six responses is presented to highlight how specific participants developed their sorting strategies. These methodologies may provide the rich data needed to further develop ecological-type models for classifying social media use and perception.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".