Exploring #nofilter Images When a Filter Has Been Used
Bibliographic record
Abstract
Many social media users rely on photo editing techniques in order to receive more positive attention (i.e., likes/comments) online. This study used a mixed methods approach to conduct a descriptive analysis of #nofilter use by Instagram users. By using #nofilter users are making a point that they did not edit/manipulate their images. Of particular interest were those who used #nofilter but did filter their images. A text analysis of 18,366 images was conducted using Netlytic, reveling the largest content category as ‘appearance'. A content analysis was used to examine authors of #nofilter images whom did use a filter, and photo-coding scheme for this group of images was implemented. Of 18,366 images collected that used #nofilter, 12% (N=1630) did in fact use a filter. Listwise deletions were conducted and 1344 images remained. Results suggest the majority of accounts were personal, and belonged to females and of the images, majority had people in them. People using #nofilter do in fact filter their images and research into the reasons for deceit on social media is needed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".