Digital Affective Encounters: The Relational Role of Content Circulation on Social Media
Bibliographic record
Abstract
Abstract One of the most ubiquitous practices on social media is sharing content with others to show affection or affirm an interpersonal connection. Extant consumer research has examined the circulation of objects fueled by desire or as repositories and carriers of emotion and value. The article extends this work to understand how consumer relationships are shaped through the creation, consumption, and circulation of digital objects imbued with affect. Drawing upon interviews with both managers and followers of animal accounts and netnographic data of animal content on Instagram, this work theorizes how digital affective encounters on social media transpire through the circulation of animal content. The findings highlight the processes through which content is continuously imbued with affective cues to manage parasocial and interpersonal relationships. While affect-laden content can serve as a relational token when shared between friends and family, it can also be captured and modified by large accounts. As a result, the affective force of this content can reach mass audiences and become memetic. Our article shows the significant role of affect as a mobilizing force of digital affective networks. Beyond animal content, the framework is transferable to the circulation of other social media content and consumer–influencer relationships.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".