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Better safe than sorry: a study on older adults' credibility judgments and spreading of health misinformation.

Jia ZhouHonglian XiangBingjun Xie
Published in: Universal access in the information society (2022)
The online world is flooded with misinformation that puts older adults at risk, especially the misinformation about health and wellness. To understand older adults' vulnerability to online misinformation, this study examines how eye-catching headlines and emotional images impact their credibility judgments and spreading of health misinformation. Fifty-nine older adults aged between 58 and 83 years participated in this experiment. Firstly, participants intuitively chose an article for further reading among a bunch of headlines. Then they viewed the emotional images. Finally, they judged the credibility of health articles and decided whether to share these articles. On average, participants only successfully judged 41.38% of health articles. Attractive headlines not only attracted participants' clicks at first glance but also increased their credibility judgments on the content of health misinformation. Although participants were more willing to share an article they believed than not, 62.5% of the articles they want to share were falsehoods. Older adults in this study were notified of possible falsehoods in advance and were given enough time to discern misinformation before sharing. However, these efforts neither lead to a high judgment accuracy nor a high quality of information that they wanted to share. That may be on account of eye-catching headlines which misled participants into believing health misinformation. Besides, the most older adults in this study may follow the "better safe than sorry" principle when confronted with health misinformation, that is to say they would rather trust the misinformation to avoid health risks than doubt it.
Keyphrases
  • social media
  • health information
  • public health
  • healthcare
  • physical activity
  • mental health
  • health promotion
  • risk assessment
  • machine learning
  • human health
  • convolutional neural network
  • quality improvement