No News is Not Good News: The Implications of News Fatigue and News Avoidance in a Pandemic World
Bibliographic record
Abstract
In an era dominated by a constant flow of grim news, news fatigue is becoming more recognized as a serious concern, even a health risk. Around-the-clock reports on the pandemic can seem unavoidable, along with ongoing coverage of political upheaval, climate change, and other major global issues. For some, the weight of the world’s news becomes too much. A 2019 pre-pandemic survey of 12,000 American adults by the Pew Research Center found 66% admitting they were “worn out” by the sheer amount of news available to them. News fatigue can translate into a desire to consume less news in an effort to preserve and protect one’s mental health. A Pew Research Center survey in April 2020 determined 71% of adult Americans say they need to “take breaks from COVID-19 news” while 43% said the news “leaves them feeling worse emotionally”. The World Health Organization addressed the concerns about the impact of the news onslaught in the “Mental Health Tips” section of its website. The WHO offers this advice to the public: “Try to reduce how much you watch, read or listen to news that makes you feel anxious or distressed”. Growing numbers are heeding this advice and reducing their news consumption. Some are opting for no news whatsoever as a means of coping. In May 2020, the Reuters Institute for the Study of Journalism at Oxford University examined the “infodemically vulnerable” in Britain, those who chose to reduce consumption of COVID-19 related news. More than one-fifth of those surveyed said: “they often or always actively try to avoid the news,” with the majority citing the impact on their mood. While mental health concerns appear to be the primary reason behind the increase in avoidance, growing distrust in mainstream media is also cited. While not a new phenomenon, the skepticism surrounding journalism was exacerbated during the pandemic as anti-vaccination advocates and conspiracy theorists questioned the validity and accuracy of the COVID-19 facts shared by news organizations, even governments. In this analysis of research, interviews, news articles, and social media content, I will advance recommendations for journalism researchers seeking to understand these issues. I will also propose strategies for journalists and news organizations seeking to navigate the issues and find solutions to help their embattled profession survive and recover. Keywords: journalism, news avoidance, news fatigue, misinformation, trust
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".