In this paper, we present a process for collecting and filter-ing relevant data for epidemiological surveillance of meningitis. We focuson the African meningitis belt stretching from Senegal to Ethiopia. Thisstudy aims to fill the data gap for the early detection of epidemics basedon the analysis of social media. Our approach is based on previous workthat showed that social media analysis contributes significantly to thesurveillance of epidemics. It uses IDOMEN (Infectious Disease Ontol-ogy for MENingitis) a meningitis domain ontology and a SKOS resourcemeningVocab (meningitis vocabulary). IDOMEN is an extension of theInfectious Disease Ontology (IDO). The SKOS resource meningVocab isbuilt from a corpus of meningitis tweets from social media. We align theIDOMEN ontology and the SKOS resource meningVocab for collectionand filtering tweets containing data relevant to meningitis in a perspec-tive of epidemiological surveillance. Tweets are collected via the TwitterAPI on the basis of a list of terms related to meningitis. They are thenannotated using these two resources and filtered using the rules of thedomain (for example, the rules characterizing situations suggestive ofbacterial meningitis: fever AND purpura AND headache)

Ontology · SKOS Resource · Social media · Epidemicintelligence · Meningitis