Unraveling threats in parasocial relationships: a study on social media influencers
Bibliographic record
Abstract
Purpose With growing concerns about users’ well-being on social media, research stresses the importance of threat appraisals as a crucial first step in motivating self-protective actions. This study, in view of the prevalence of parasocial relationships between followers and social media influencers, aims to unravel the complex dynamics of followers’ threat perceptions within these relationships. Specifically, it examines how factors such as perceived self-efficacy to disengage and the positive affect of social media use influence threat appraisals. Design/methodology/approach A theoretical model is proposed based on appraisal theory to examine the impact of parasocial relationships on threat perception in engagement. It is empirically tested with data from 186 Instagram users. Findings The study reveals an overall positive relationship between parasocial relationships and perceived threat. This relationship is moderated by followers’ perception of self-efficacy to disengage – followers with a high sense of self-efficacy to disengage experience a decrease in threat perception as their parasocial relationships strengthen, whereas followers with a low sense of self-efficacy to disengage report an increase in threat perception with higher levels of parasocial relationships. This interplay is pronounced when followers experience average or below-average levels of positive affect on social media but diminishes when the positive affect is high. Originality/value This work contributes insights into social media influencers, threat appraisal dynamics and digital well-being research. Bridging a critical gap in existing knowledge, the study identifies the pivotal roles of followers’ self-efficacy to disengage and positive affect in shaping their threat appraisals toward parasocial relationships with social media influencers. This not only advances theoretical frameworks but also enhances our understanding of the nuanced dynamics of user reactions to parasocial engagements. Our findings offer practical insights for researchers, practitioners and platform developers aiming to cultivate healthy and responsible social media engagement in the digital era, ultimately contributing to individual well-being.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".