Something Ordinary to Something Navy- Exploring Influencer Arielle Charnas’ use of Parasocial Interaction Techniques on Instagram
Bibliographic record
Abstract
The purpose of this research paper is to explore ideas that may highlight why Instagram users may be interested in following the lives and daily activities of social media influencers on Instagram. Social media influencers have been defined as “independent third party endorsers who have developed sizeable social networks by sharing details about their personal lives, experiences, and opinions publicly through texts, pictures, videos” (Ki & Kim, 2019, p. 905). Research also suggests that if successful, influencers can become “opinion leaders who communicate with a sizeable social network of people following them” (Boerman, 2020, p. 199). Arielle Charnas is a New York City fashion influencer with over one million followers on Instagram, and she will be used as a case to study alongside parasocial interaction to explore how, as an influencer, she incorporates techniques from this theory to establish a connection with her followers. This MRP supports research that proposes that the two established parasocial interaction techniques of perceived interactivity and openness “should increase feelings of parasocial interaction” (Labrecque, 2014, p. 136). Moreover, the analysis of Arielle’s Instagram supports the concept that the development of feelings of a parasocial relationship can be enhanced when “media presentations resemble interpersonal interaction” (Perse & Rubin, 1989, p. 60).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".