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The declaration of COVID-19 as a pandemic has largely amplified the spread ofrelated information on social media, such as Twitter, Facebook, andWeChat.Unlike the previous studies which focused on how to detect themisinformation or fake news related toCOVID-19, we investigate how the diseaseand information co-evolve in the population. We focus onCOVID-19and itsinformation during the period when the disease was widely spread in China,i.e., from January 25th to March 24th, 2020. We first explore how the diseaseand information co-evolve via the spatial analysis of the two spreadingprocesses. We visualize the geo-location of both disease and information at theprovince level and find that disease is more geo-localized compared toinformation. We find a high correlation between the disease and informationdata, and also people care about the spread only when it comes to theirneighborhood. Regard to the content of the information, we find that positivemessages are more negatively correlated with the disease compared to negativeand neutral messages. Additionally, we introduce machine learning algorithms,i.e., linear regression and random forest, to further predict the number ofinfected using different disease spatial related and information-relatedcharacteristics. We obtain that the disease spatial related characteristics ofnearby cities can help to improve the prediction accuracy. Meanwhile,information-related characteristics can also help to improve the predictionperformance, but with a delay, i.e., the improvement comes from using, forinstance, the number of messages 10 days ago, for disease prediction. Themethodology proposed in this paper may shed light on new clues of emerginginfections

Exploring the effect of social media and spatial characteristics during the COVID-19 pandemic in China