Methods for Coding Tobacco-Related Twitter Data: A Systematic Review.

Brianna A Lienemann Jennifer Beth Unger Tess Boley Cruz Kar-Hai Chu

Published in: Journal of medical Internet research (2017)

Standards for data collection and coding should be developed to be able to more easily compare and replicate tobacco-related Twitter results. Additional recommendations include the following: sample Twitter's databases multiple times, make a distinction between message attitude and emotional tone for sentiment, code images and URLs, and analyze user profiles. Being relatively novel and widely used among adolescents and black and Hispanic individuals, Twitter could provide a rich source of tobacco surveillance data among vulnerable populations.

Keyphrases

social media
big data
electronic health record
deep learning
machine learning
data analysis
artificial intelligence
drug induced