The Impact of Different Types of Social Media Engagement on Parasocial Interactions and Relationships: A User’s Perspective
Bibliographic record
Abstract
Asymmetric interactions and relationships between celebrities and followers, referred to as Parasocial Interactions (PSI) and Parasocial Relationships (PSR), respectively, have been extensively studied within the context of TV, radio and print media. However, within the Social Media (SM) ecosystem - where follower engagement and experience flow seamlessly across multiple SM tools/platforms - the nature of PSI and PSR is changing and research within this context is still nascent. Using Identity Theory and Motivational Theory, this dissertation analyses the impact active/passive engagement and compulsive use of the SM ecosystem can have in the formation of PSI and PSR. Specifically, the goal of this research is to understand how different types of engagement (passive and active) can influence parasocial relationships (friendship and love) through the mediation of cognitive and behavioural parasocial interactions. A model is proposed and validated with 294 respondents. The findings show that passive engagement in the SM ecosystem does not impact PSI/PSR while active engagement significantly impacts PSI and the consequent PSR formation. Compulsive use of the SM ecosystem strongly attenuates the relationships between PSI and PSR. Contributions and implications for both theory and practice are discussed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".