The Semantics of Social Media Reactions among Baby Boomers, Gen X, Millennials, and Gen Z: An Exploratory Sequential Mixed-Methods Study
Bibliographic record
Abstract
In the fast-changing world of social media, reactions, including "like," "love," "haha," "wow," "care," "sad," and "angry," are not only icons but also a modern vocabulary of emotions.To what degree do different age demographics comprehend and utilize these phrases within the linguistic framework?This research reveals a significant gap in understanding how emojis are interpreted across different age cohorts, including Baby Boomers, Generation X, Millennials, and Generation Z.To bridge this gap, a sequential exploratory mixed-methods approach was utilized, beginning with qualitative theme analysis from interviews, followed by a quantitative survey with 660 respondent across four age demographics.The findings indicate notable generational differences: Millennials and Gen Z interpret reactions with greater flexibility, irony, humor, or intense emotion, while Baby Boomers and Gen X regard them literally as direct emotional support.Moreover, they use these emojis to downplay the most severe judgments and bring in social bonding.These findings have implications for the growth of intergenerational communication, providing platform providers, marketers, and educators with essential knowledge to overcome generational differences in online contexts.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".