Social network data and epidemiological intelligence: A case study of avian influenza

Schaeffer Camille, Interdonato Roberto, Lancelot Renaud, Roche Mathieu, Teisseire Maguelonne. 2022. Social network data and epidemiological intelligence: A case study of avian influenza. International Journal of Infectious Diseases, 116, suppl., p. S99 International Meeting on Emerging Diseases and Surveillance (IMED 2021), s.l., 4 Novembre 2021/6 Novembre 2021.

https://doi.org/10.1016/j.ijid.2021.12.233

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Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Psychologie-éthologie-ergonomie

Résumé : Purpose: Event Based Surveillance (EBS) systems detect and monitor diseases by analysing articles from online newspapers and reports from health organizations (e.g. FAO, OIE, etc.). However, they partially integrate data from social networks, even though these data are present in large quantities on the web. The purpose of this study is to exploit social network data, such as Twitter and YouTube, to provide epidemiological and additional information for Avian Influenza surveillance. Methods & Materials: In this context, we propose new text-mining approaches combining lexical rules and statistical approaches, in order to normalise textual data from Social Network ('h5 n8'→'H5N8') and to correct errors from YouTube transcriptions (e.g. 'birth flu'→'bird flu'). Another challenge consists of extracting epidemiological events automatically by identifying spatial entities (Where?), thematic entities (What?), and temporal information (When?). For this, we extended Named Entity Recognition (NER) tools like spaCy. Results: We collected 100 automatic transcripts of YouTube videos and 268 tweets, in English, dealing with avian influenza, thanks to dedicated API. We obtain encouraging results (i.e. accuracy around 0.6) in order to recognise automatically epidemiological information (e.g. hosts, symptoms etc.) in textual data contents. Extraction of spatial information obtains better results (i.e. accuracy around 0.8). Conclusion: The final objective of the study consists of linking social media data based on these entities with official information from health organisations, for the improvement of epidemiological monitoring.

Mots-clés Agrovoc : surveillance épidémiologique, réseaux sociaux, fouille de textes, épidémiologie, grippe aviaire, système de surveillance, analyse de données

Mots-clés libres : Event-based surveillance, Text Mining, Social network, Epidemic intelligence, Avian influenza

Classification Agris : U10 - Informatique, mathématiques et statistiques
C30 - Documentation et information
L73 - Maladies des animaux

Champ stratégique Cirad : CTS 4 (2019-) - Santé des plantes, des animaux et des écosystèmes

Agences de financement européennes : European Commission

Programme de financement européen : H2020

Projets sur financement : (EU) MOnitoring Outbreak events for Disease surveillance in a data science context

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