Associations Between Valenced News and Affect in Daily Life: Experimental and Ecological Momentary Assessment Approaches
Bibliographic record
Abstract
=12.05) participants, we tested the association between valenced news and affect using a 14-day, smartphone-based ecological momentary assessment protocol consisting of two components: 1) a once-per-day experimental protocol in which participants were exposed to good news and bad news stories and 2) a four-times-per-day protocol capturing ecological fluctuations in news consumption. Across both protocols, we replicate findings that consumption of positively valenced news is associated with increased positive affect and decreased negative affect while consumption of negatively valenced news is associated with increased negative affect and decreased positive affect. By integrating the ecological momentary assessment data with network science methodologies, news selection and news effects were modeled simultaneously, uncovering selection processes whereby current positive affect, but not negative affect, predicted future valenced news consumption. Altogether, findings indicate that everyday news consumption influences positive and negative affect and may serve mood management functions for positive but not negative affect.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".