Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance.

ABSTRACT: We investigate the use of Twitter data to deliver signals for syndromic surveillance in order to assess its ability to augment existing syndromic surveillance efforts and give a better understanding of symptomatic people who do not seek healthcare advice directly. We focus on a specific syndrome-asthma/difficulty breathing. We outline data collection using the Twitter streaming API as well as analysis and pre-processing of the collected data. Even with keyword-based data collection, many of the tweets collected are not be relevant because they represent chatter, or talk of awareness instead of an individual suffering a particular condition. In light of this, we set out to identify relevant tweets to collect a strong and reliable signal. For this, we investigate text classification techniqu

SUBMITTER: Edo-Osagie O 

PROVIDER: S-EPMC6638773 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature