Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
Misinformation undermines a shared understanding of the complexity of climate change and impedes public support for mitigation policies to build resilience. This paper reports an empirical study (N=398) of U.S. adults’ accuracy and bias in identifying true and false headlines related to climate change. The headlines were evenly balanced such that half were true and half false (fake news); likewise, half emphasized, and half questioned the urgency of climate change. Respondents indicated whether they believed each of the 24 headlines was true or false and provided confidence ratings. Pooled ROC analyses suggested moderate accuracy (AUC = 0.68) and a bias (c=0.09) favoring accuracy for false headlines (specificity=0.67) and attenuating the accuracy rate for true headlines (sensitivity = 0.60). Regression analysis on individual Signal Detection Theory (SDT) performance measures revealed that political liberalism and actively open-minded thinking (AOT) positively predicted accuracy (AUC), while climate change anxiety (CCAS) positively predicted bias.
Recommended Citation
Fong, Mia and John, Richard, "Motivated Bias in Detecting Climate Change Misinformation" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 4.
https://aisel.aisnet.org/hicss-57/dg/disaster_resilience/4
Motivated Bias in Detecting Climate Change Misinformation
Hilton Hawaiian Village, Honolulu, Hawaii
Misinformation undermines a shared understanding of the complexity of climate change and impedes public support for mitigation policies to build resilience. This paper reports an empirical study (N=398) of U.S. adults’ accuracy and bias in identifying true and false headlines related to climate change. The headlines were evenly balanced such that half were true and half false (fake news); likewise, half emphasized, and half questioned the urgency of climate change. Respondents indicated whether they believed each of the 24 headlines was true or false and provided confidence ratings. Pooled ROC analyses suggested moderate accuracy (AUC = 0.68) and a bias (c=0.09) favoring accuracy for false headlines (specificity=0.67) and attenuating the accuracy rate for true headlines (sensitivity = 0.60). Regression analysis on individual Signal Detection Theory (SDT) performance measures revealed that political liberalism and actively open-minded thinking (AOT) positively predicted accuracy (AUC), while climate change anxiety (CCAS) positively predicted bias.
https://aisel.aisnet.org/hicss-57/dg/disaster_resilience/4