Exploring Fan-Celebrity Parasocial Relationships on Cameo
Bibliographic record
Abstract
This thesis examines the phenomenon of parasocial relationships between fans and celebrities in the context of the video-sharing platform, Cameo.By analyzing the reviews left by fans on Cameo, this research investigates how fans engage with and evaluate their parasocial connections with celebrities.The study combines the domains of parasocial relational work and linguistic evaluation theory to explore the transactional practices and linguistic expressions of approval or disapproval within celebrity-fan interactions.The analysis focuses on the reviews left by fans on celebrities' profiles, highlighting their satisfaction or dissatisfaction with the personalized videos received.The study includes close readings of the data in order to build up generalizations about the findings, then incorporates n-gram analysis and sentiment analysis to compare the higher and lower reviews.It aims to determine fans' level of satisfaction with the product and investigate if expectations and satisfaction vary across different types of celebrities, specifically film and television actors, reality television personalities, and athletes.The analysis of fan reviews on Cameo demonstrates that fan satisfaction is closely tied to the perception of personalization in the videos.Fans appreciate personalized content that reinforces their parasocial bonds, whereas a lack of personalization leads to negative evaluations.Notably, fans show a preference for celebrities who conform to their specific requests, prioritizing the fulfillment of their expectations over authenticity.Overall, this study contributes to our understanding of the interplay between parasocial relational work, linguistic evaluation, and fan culture in the context of the Cameo platform.It sheds light on the factors that influence fan satisfaction, highlighting the importance of personalization and the role of fan expectations in evaluating the products provided by celebrities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.007 |
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; both teacher heads agree on what is shown here.
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".