Is the News Always Negative? Using Deep Learning to Track News Sentiment During the Covid-19 Pandemic
Bibliographic record
Abstract
The news is notorious for its tendency to focus on negative events, giving rise to the adage, "If it bleeds, it leads." In this study, I examine whether news coverage during the Covid-19 pandemic mainly focused on negative events while downplaying positive developments such as decreasing Covid-19 cases. Utilizing a state-of-the-art fine-tuned language model, I analyzed the sentiment of over 900,000 Covid-19 related news articles from March 2020 to April 2022 across the United States, Canada, and the United Kingdom. The results indicate that the news is far more negative than positive—even when Covid-19 cases and hospitalizations are decreasing. This negativity is most pronounced in Op-Ed articles, front-page news articles, and articles published by large news organizations (e.g., New York Times, BBC, Fox News). However, non-Op-Ed news articles do become more positive as Covid-19 cases decrease, contradicting previous research findings. These discrepancies can be attributed, in part, to differences in model accuracy, as the model I trained is approximately 20% more accurate than other models used in the literature. Further, when dividing U.S. news by the publisher's political ideology, clear differences emerge: both left-wing and right-wing sources are much more negative than centrist news sources. Surprisingly, these differences in sentiment are about as large as the difference between regular news and Covid-19 news sentiment, indicating substantial differences in news reporting across political lines. These findings provide new insights into news reporting patterns during the pandemic and carry important implications for public health messaging and news reporting practices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".