Exploring the motivation of affect management in fostering social media engagement and related insights for branding
Bibliographic record
Abstract
Purpose The purpose of this paper is to present an affect-based perspective to explain levels of social media engagement. Design/methodology/approach This study uses face-to-face long interviews and online observation of the Facebook profiles of respondents over an eight-month period. Findings Social media engagement varies depending on a user’s current and desired affective state. When individuals are in a low to moderately aroused negative affective state (such as feeling bored or upset), individuals tend to spend time passively consuming content: the lowest level of engagement. In a low to moderately aroused positive mood state (such as happiness), users both passively consume and actively participate with relevant content by liking and commenting on existing content. When users are in a highly aroused positive affective state, the propensity to create original content is greater, reflecting the highest level of engagement. When users are in a highly aroused negative affective state (such as being angry at a brand), users are motivated to vent on social media to manage the mood. Conversely, when users are in a highly aroused negative affective state related to personal trauma, the avoidance of engagement on social media is evident. Practical implications Brands can increase the likelihood of consumers creating positive consumer–brand stories offline and online by priming consumer affect. Originality/value This study explores how a desired affective state motivates varying levels of user engagement with different types of content on social media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".