Mining Physicians’ Opinions on Social Media to Obtain Insights Into COVID-19: Mixed Methods Analysis - data analysis - categories codebook

This is the result from the automatic coding. 

The 8 categories defined are "Actions and Recommendations", "Fighting Misinformation", "Healthcare Systems", "Information and Knowledge", "Symptoms and Illness", "Immunity", "Testing", and "Infection and Transmission."

University of California, Berkeley

This was a mixed methods paper in which both data analytics and qualitative analysis was used. 

To start the analysis, they first analyzed a random subset of 250 of the 10,096 tweets to check if the tweets were actually relevant to the COVID-19 pandemic. The researchers themselves independently labeled these tweets as relevant or irrelevant , and they established interrater reliability and got a Fleiss score of 0.628, meaning they had "substantial agreement." From this, they used NVivo to perform automatic coding of the relevant tweets. As this gave them the main code and subcode analysis, they were able to identify 8 different categories for these tweets plus one category for irrelevant tweets. 

For quantitative analysis, Crimson Hexagon was used to look at the social media statistics. 

Mining Physicians’ Opinions on Social Media to Obtain Insights Into COVID-19: Mixed Methods Analysis - data analysis

Wahbeh, A., Nasralah, T., Al-Ramahi, M., & El-Gayar, O. (2020). Mining physicians’ opinions on social media to obtain insights into COVID-19: mixed methods analysis. JMIR public health and surveillance, 6(2), e19276. https://publichealth.jmir.org/2020/2/e19276/?utm_source=TrendMD&utm_medium=cpc&utm_campaign=JMIR_TrendMD_0

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