Table 2.

Research on Disease Outbreaks on Social Media

Author	Disease outbreak	Social media	Methods	Findings
Mollema et al. (2015)	Measles	Twitter and others	Thematic analysis	People on Twitter cared about disease transmission, preventive actions, and vaccination; governments needed to promote vaccination acceptability.
Fung et al. (2013)	MERS-CoV & H7N9	Weibo	Statistical analysis on the number of Weibo posts	Weibo users reacted to the disease outbreak significantly, and people paid more attention to the H7N9 outbreak.
Ye, Li, Yang, and Qin (2016)	Dengue	Weibo	Analysis on the numbers of posts and spatial information	Spatially and temporally, there was a correlation between the number of posts and disease development trends.
Chew and Eysenbach (2010)	H1N1	Twitter	Manual and automated coding	Several sentiments, including confusion, humor, risk, and so on, were discovered, among which humor was the most popular sentiment.
Ye et al. (2016)	Influenza	Twitter	Modeling	A prediction model built on Twitter data could be used for influenza outbreak alerts.
Zhang et al. (2015)	H7N9	Weibo	Analysis on the number of Weibo posts and the number of new confirmed cases	There was a positive correlation between discussion and disease outbreak level, and Weibo served as a good medium to promote communications of public health.
Li et al. (2020)	COVID-19	Weibo	Machine learning algorithms	Weibo posts were classified into seven categories of situational information. Useful text features should be helpful in building an emergence response system.