ABSTRACT
The large volume of data generated on social networks is used by companies to monitor public opinion about their products and services. These data may contain useful information for health surveillance, such as in assessing the impact of public policies or identifying fake news. This work presents results of studies that demonstrate how analysis of data from social networks may be applied to surveillance activities, using the covid-19 pandemic as a case study. An approach based on data science was used, with information extracted through machine learning algorithms. Results indicate that this approach can reveal useful information for surveillance activities, providing a real-time view of aspects related to the pandemic.
KEYWORDS:
Health surveillance; Social networks; Machine learning; Covid-19