Null effects of news exposure: a test of the (un)desirable effects of a ‘news vacation’ and ‘news binging’
Bibliographic record
Abstract
Abstract Democratic theorists and the public emphasize the centrality of news media to a well-functioning society. Yet, there are reasons to believe that news exposure can have a range of largely overlooked detrimental effects. This preregistered project examines news exposure effects on desirable outcomes, i.e., political knowledge, participation, and support for compromise, and detrimental outcomes, i.e., attitude and affective polarization, negative system perceptions, and worsened individual well-being. We rely on two complementary over-time experiments that combine participants’ survey self-reports and their behavioral browsing data: one that incentivized participants to take a ’news vacation’ for a week (N = 803; 6M visits) in the US, the other to ‘news binge’ for 2 weeks (N = 939; 4M visits) in Poland. Across both experiments, we demonstrate that reducing or increasing news exposure has no impact on the positive or negative outcomes tested. These null effects emerge irrespective of participants’ prior levels of news consumption and whether prior news diet was like-minded, and regardless of compliance levels. We argue that these findings reflect the reality of limited news exposure in the real world, with news exposure comprising on average roughly 3% of citizens’ online information diet.
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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 | 0.032 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".