Instagram Influencer Analysis for Top 5 Categories in Turkey
Bibliographic record
Abstract
Social media platforms have become an inevitable part of our daily lives. Companies that noticed the intense use of social media platforms started to use them as a marketing tool. Even ordinary people have become famous by social media and companies have been sending their products to them to try and advertise. Many people have gained a considerable amount of money in this way and today new jobs are emerged like "Youtuber" and "Instagram Influencer". Therefore, ordinary people realized the power of social media and many people started to strength their digital identity over social media. The question raising in people’s mind is that “What is the difference between the influencers and the ordinary people who have also digital identity over social media?”. This study examined Instagram influencers for five categories namely fashion, makeup, photography, travel, and fitness in Turkey. As an exploratory study, the relationship between the influencers’ average number of posts, the number of likes, the number of views, the number of comments, number of followers, and the number of following were examined. As well as the engagement rates of the followers to the influencers were calculated. In addition, the words they mostly used in the captions of the posts were examined.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".