Media Coverage of Mass shootings and Attitudes Towards Muslims
Bibliographic record
Abstract
Using television news coverage of American mass shootings, I examine how Muslim perpetrators of violence are covered differently in liberal and conservative media. Furthermore, I examine the consequences of this coverage, including its effects on prejudice towards Muslims and beliefs about the extent to which Muslims are responsible for acts of violence. In Study 1, I show that between 2010 and 2020, television news networks allocated more coverage to Muslim than non-Muslim mass shooters, even when statistically controlling for other factors influencing coverage (e.g. fatalities, location). The increase in coverage for Muslim shooters was larger for more politically conservative news networks. In Study 2, I test whether news coverage of Muslim mass shootings can increase negative attitudes towards Muslims. Across five different experiments with a total of 3331 participants, I did not find evidence that this is the case. The remaining studies examine whether media coverage can impact beliefs about the role Muslims play in mass shootings. In Study 3, I find that public beliefs about mass shootings mirror the picture presented in television news: people overestimate the percentage of mass shooters who are Muslim, and this overestimation is largest among frequent viewers of news networks that allocate more coverage to Muslim shooters. Study 4 used an experiment to demonstrate that exposure to news coverage of a Muslim mass shooter increases the extent to which people overestimate the percentage of mass shooters who are Muslim. Study 5 replicates this finding by comparing responses to media coverage of the 2021 shootings in Boulder, Colorado and Atlanta, Georgia, two contemporaneous mass shootings with Muslim and Christian perpetrators. Finally, Study 6A and 6B find that, because many people assume an unidentified mass shooter is likely to be Muslim, exposure to news coverage of an unidentified shooter also increases the extent to which people overestimate of the percentage of mass shooters who are Muslim. Overall, I demonstrate that major news networks provide systematically different media coverage of mass shootings when the perpetrator is Muslim. This coverage fuels misperceptions about the role Muslims play in causing mass shootings and may misdirect efforts to effectively address mass violence.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".