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The spread of fake news related to COVID-19 is an infodemic that leads to a public health crisis. Therefore, detecting fake news is crucial for an effective management of the COVID-19 pandemic response. Studies have shown that machine learning models can detect COVID-19 fake news based on the content of news articles. However, the use of biomedical information, which is often featured in COVID-19 news, has not been explored in the development of these models. We present a novel approach for predicting COVID-19 fake news by leveraging biomedical information extraction (BioIE) in combination with machine learning models. We analyzed 1164 COVID-19 news articles and used advanced BioIE algorithms to extract 158 novel features. These features were then used to train 15 machine learning classifiers to predict COVID-19 fake news. Among the 15 classifiers, the random forest model achieved the best performance with an area under the ROC curve (AUC) of 0.882, which is 12.36% to 31.05% higher compared to models trained on traditional features. Furthermore, incorporating BioIE-based features improved the performance of a state-of-the-art multi-modality model (AUC 0.914 vs. 0.887). Our study suggests that incorporating biomedical information into fake news detection models improves their performance, and thus could be a valuable tool in the fight against the COVID-19 infodemic. 

This research examines how the operating expenditures of America’s state park systems will be affected by a continued growth in attendance consistent with observed trends as well as potential climate futures. We construct a longitudinal panel dataset (1984–2017) describing the operations and characteristics of all 50 state park systems. These data are analyzed with a time-varying stochastic frontier model. Estimates from the model are used to forecast operating expenditures to midcentury under four different scenarios. The first scenario assumes annual attendance within each state park system will continue to grow (or decline) at the same average annual rate that it has over the period of observation. The subsequent scenarios assume statewide annual mean temperatures will increase following the RCP2.6, RCP4.5, and RCP8.5 greenhouse gas emissions trajectories. Operating expenditures under a scenario where annual growth in attendance stays consistent with observed trends are forecasted to increase 756% by midcentury; this is an order of magnitude larger than projected expenditures under any of the climate scenarios. The future climate change scenarios yielded increases in operating expenditures between 25% (RCP2.6) and 61% (RCP8.5) by 2050. Attendance is the single largest factor affecting the operations of America’s state park systems, dwarfing the influence of climate change, which is significant and nontrivial. The future of America’s state park systems will depend upon increased support from state legislatures, as well as management actions that generate funds for the maintenance of existing infrastructure and facilities, and the provisioning of services.