Allergies are one of the most common chronic illnesses in the world. The prevalence of social media allows people to express their opinions and exchange information including symptoms of personal health. Mining those publicly accessible health-related data on social media, such as Twitter, offers a unique approach to get valuable healthcare insights.
In this paper, a multi-component data mining framework was developed to collect Twitter data, detect time series patterns, discover topics of interest about allergies, and analyze the contents of tweets. From the extracted 2.2 million tweets in 2019, my experimental results show that allergy-related tweet volume is strongly correlated to the pollen data (r = .699, p < .01). Also, 152 unique topics are identified with a -28.36 perplexity score and a .67 coherence score. Furthermore, many linguistic dimensions such as the sentiment are analyzed to learn about the tweet contents. I consider this to be one of the many studies examining a large-scale social media stream to deeply analyze allergy activities. And with the growing social media, publicly available data such as Twitter posts can be used to support healthcare practitioners and social scientists in better understanding common public opinions, not just allergies.Master of Scienc
