Social Media Influencers and Instagram Storytelling: Case Study of Singapore Instagram Influencers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While the use of social media influencers (SMIs) by brands is becoming more widespread, the academic literature about SMI communication is still scarce. This is one of the first studies on SMI brand storytelling, using data mining and natural language processing to understand how SMIs tell brand stories on Instagram, what kinds of stories they tell, and the impact they have on follower engagement. The findings show that the “rise-fall” emotional arc was the most common story arc used by SMIs. In addition, SMIs frequently used the first-person perspective and featured themselves as the protagonists in their stories. Last, SMIs who used more first-person pronouns and more positive emotions in their stories received more “likes” than those who used fewer first-person pronouns and fewer positive emotions. The paper concludes with a discussion of the study’s implications for SMI communication theory-building and practice as well as its limitations.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it