Message self and social relevance increases intentions to share content: Correlational and causal evidence from six studies
Bibliographic record
Abstract
Information sharing within social networks can catalyze widespread attitudinal and behavioral change and the chance to share information with others has been characterized as inherently valuable to people. But what are the sources of value and how might they be leveraged to promote sharing? We test ideas from the value-based virality model that the value of sharing increases when people perceive messages as more relevant to themselves and to people they know, resulting in stronger intentions to share. We extend this work by considering how sharing context—broadcasting to a wide audience or narrowcasting directly to someone—may alter these relationships. Six online studies with adults in the United States (N participants=3,727; messages=362; message ratings=30,954) showed robust evidence that self and social relevance are positively and uniquely related to sharing intentions within- and between-person. Specification curve analysis showed these relationships were consistent across message content (COVID-19, voting, general health, climate change), medium (social media post and news articles), and sharing context (broad- and narrowcasting). A preregistered experiment showed that manipulating the self and social relevance of messages through a framing manipulation causally increased sharing intentions. These causal effects were mediated by changes in both self and social relevance, but the relative strength of the causal pathways differed depending on sharing context. These findings extend existing models of information sharing, and highlight self and social relevance as psychological mechanisms that motivate information sharing that can be targeted to promote sharing across 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 | 0.017 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".