Observational infoveillance study

• Used Python to collect Covid-19-related Weibo posts from December 23, 2019 to January 30, 2020 containing certain keywords
• Categorized posts by geographic location and separated posts from Wuhan to focus on
• Chinese National Health Commission data for official case counts
• Collected a total of 115,299 posts; average of 2,956 posts per day

Quantitative analysis

• Categorized posts by calendar day
• Visualized longitudinal trends and ran simple regression models for posts per day and daily cases reported
• Interested in seeing whether simple regression models could accurately predict changes in daily case counts using the number of daily Weibo posts

Qualitative analysis

• To observe trends and common themes mentioned among users
• Binary coding approach to label posts as containing relevant Covid-19 information and news or irrelevant information
• Thematic content analysis used to determine frequently mentioned topics
• Chose most common topics as parent classifications and proceeded to categorize posts into these identifications