Using social media images as data in social science research
Bibliographic record
Abstract
We conducted a scoping review to identify and describe trends in the use of social media images as data sources to inform social science research in published articles from 2015 to 2019. The identified trends include the following: (1) there is increasing interest in social media images as research data, especially in disciplines like sociology, cultural studies, communication and environmental studies; (2) the photo sample size is often smaller than that is typically used in text-based social media analysis and usually is collected manually; (3) thematic coding, object recognition and narrative analysis are the most popular analysis methods that are often conducted manually; (4) computer vision and machine-learning technologies have been increasingly but still infrequently used and are not fit for all purposes; and (5) relatively few papers mention ethics and privacy issues, or apply strategies to address ethical issues. We identify noteworthy research gaps, and opportunities to address limitations and challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.069 | 0.269 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.046 | 0.033 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".