To Report or Not to Report? A Qualitative Analysis of Journalists’ Perspectives on Harm to Public Opinion
Bibliographic record
Abstract
Journalists face intricate decisions regarding what to publish, especially when problematic content may impact public opinion in a way that could fuel hate and/or undermine democratic attitudes. While scholarship has recognized the importance of this issue, most studies focus on published content, how citizens engage with it, and the implications of published news. In this article, we provide a fresh perspective on the crucial dilemma faced by journalists concerning their perceived impact on public opinion, by leveraging data based on 36 semistructured in-depth interviews with journalists covering Brazil's political landscape. The interviews were conducted between December 7, 2021, and July 20, 2022. Our main findings are threefold. First, we find a consensus among journalists regarding what is seen as problematic content, which is centered around threats to democratic attitudes and misinformation on critical issues. Second, we examine the rationales underpinning journalists' choices to publish problematic content, which include the concept of "competing voices," the legitimacy conferred to elected representatives (e.g., the head of a government), and journalists' fear of being viewed as left leaning and losing their audience. Third, we find that journalists who do not publish problematic content do so because they expect to negatively impact public opinion, in particular democratic attitudes, and that their reporting of hate speech may not meet ethical standards. We conclude by highlighting the complex interplay of journalistic norms and expectations regarding their impact on public opinion and the news production process.
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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 | 0.054 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".