Statistical analysis of small twitter data collection to identify dengue outbreaks - Euzebio, Carlos; Agy, Sidney; Boldorini Jr., Claudio; Porto, Lucas; Alcarás, José Renato; Martinez, Alexandre; Ruiz, Evandro (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2020: Anais do VIII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2020.11954, 2020)
This study presents an algorithmic strategy to analyze a small set of social network information to monitor the dengue disease. Previous studies have achieved similar results based on large datasets of Twitter microblogs. In this study, we successfully map dengue cases using a small data collection of tweets from a medium-size city. A set of modules were constructed to collect, categorize, and display dengue-related tweets. We compared the collected tweets with real data from confirmed dengue cases. We showed a significant correlation between the number of confirmed dengue cases and the number of dengue-related tweets, even considering such a small dataset. The results of this approach may be relevant in public health policies.