Predictors of parasocial interaction and relationships in live streaming
Bibliographic record
Abstract
The purpose of the current article was to explore parasocial phenomena in the unique and interactive context of live streaming. Specifically, the predictors of parasocial interactions (PSIs) and parasocial relationships (PSRs) were compared. In the past, the terms ‘parasocial interaction’ and ‘parasocial relationship’ have been used interchangeably, even though they are distinct constructs – which has confused researchers’ understanding of these phenomena. The current study aims to begin to disentangle our understanding of these two constructs by studying the predictors for each construct separately. An online survey was utilized to collect data on PSRs, PSIs, and various parasocial predictors that fell into three categories: streamer (source) characteristics, viewer characteristics, and behavioral (relationship) characteristics. Results indicate that streamer characteristics were the most important predictors of both PSIs and PSRs in the live streaming context, although characteristics of the viewer and relationship were also influential. These findings indicate that message sources can modify their content to encourage parasocial phenomena in their audience. This is encouraging, as research suggests that parasocial phenomena lead to many positive repercussions for the media and so are generally considered a goal of media personae.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".