Roche Mathieu.
2021. Mining online and social media to analyse epidemic periods.
In : IMED - International Congress on Infectious Diseases. ISID (International society of infection diseases)
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Version publiée
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Accès réservé aux agents Cirad Utilisation soumise à autorisation de l'auteur ou du Cirad. IMED_abstract_Mathieu_Roche.pdf Télécharger (182kB) | Demander une copie |
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Version publiée
- Anglais
Accès réservé aux agents Cirad Utilisation soumise à autorisation de l'auteur ou du Cirad. IMED2021_MathieuRoche.pdf Télécharger (66MB) | Demander une copie |
Matériel d'accompagnement : 1 diaporama (20 vues)
Note générale : Mathieu Roche est l'expert invité du Cirad
Résumé : The vocabulary used in media (e.g. news) and social media (e.g. Twitter) on a disease changes according to the period. In this context, text-mining and terminology extraction tasks can be used to analyse epidemic periods of diseases. Moreover, we have to take into account this knowledge in order to improve event-based surveillance (EBS) systems. Text-mining and machine learning approaches can be integrated in different steps of EBS systems for disease-based and symptom-based surveillance: data acquisition, information retrieval (i.e. identification of relevant documents), information extraction (i.e. extraction of symptoms, locations, dates, diseases, hosts, etc.), and visualisation. This work highlights the use of text-mining approaches related to COVID- 19 (i) for surveillance systems (i.e. web crawling and information extraction tasks) and (ii) for spatio-temporal analysis of tweets.
Mots-clés libres : Epidemic intelligence, Event-based surveillance, Data science, Text Mining, PADI-Web
Auteurs et affiliations
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Roche Mathieu, CIRAD-ES-UMR TETIS (FRA)
ORCID: 0000-0003-3272-8568
Source : Cirad-Agritrop (https://agritrop.cirad.fr/600509/)
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