“Toxic atmosphere effect”: Uncivil online comments cue negative audience perceptions of news outlet credibility
Bibliographic record
Abstract
Uncivil user comments have been found to have a negative effect on how people perceive an issue featured in the news, a news story, or a journalist who reports a news story. To advance this line of research, we draw on expectancy violations theory and the concept of heuristic cues to theorize the toxic atmosphere effect . We theorize that incivility in online comment threads could pose an even larger challenge to news organizations by cuing news audience members to perceive an entire news outlet—not just an individual story—as lacking in credibility. Based on two experiments in the United States (Study 1, n = 520; Study 2, n = 1056), we show that exposure to incivility can lead people to perceive a news outlet as less credible even though the incivility did not directly attack the news outlet. Such effects hold true even when people are exposed to comment threads in which the first several comments are civil. Democratic and business implications are discussed.
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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 | 0.002 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".