Seasonal Web Search Query Selection for Influenza-Like Illness (ILI) Estimation

Niels Dalum Hansen; Kåre Mølbak; Ingemar Johansson Cox; Christina Lioma

doi:10.1145/3077136.3080760

Seasonal Web Search Query Selection for Influenza-Like Illness (ILI) Estimation

Niels Dalum Hansen, Kåre Mølbak, Ingemar Johansson Cox, Christina Lioma

Datalogisk Institut

6 Citationer (Scopus)

Abstract

Influenza-like illness (ILI) estimation from web search data is an importantweb analytics task. The basic idea is to use the frequencies of queries in web search logs that are correlated with past ILI activity as features when estimating current ILI activity. It has been noted that since influenza is seasonal, this approach can lead to spurious correlations with features/queries that also exhibit seasonality, but have no relationship with ILI. Spurious correlations can, in turn, degrade performance. To address this issue, we propose modeling the seasonal variation in ILI activity and selecting queries that are correlated with the residual of the seasonal model and the observed ILI signal. Experimental results show that re-ranking queries obtained by Google Correlate based on their correlation with the residual strongly favours ILI-related queries.

Originalsprog	Engelsk
Titel	SIGIR '17 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
Forlag	Association for Computing Machinery
Publikationsdato	7 aug. 2017
Sider	1197-1200
ISBN (Elektronisk)	978-1-4503-5022
DOI	https://doi.org/10.1145/3077136.3080760
Status	Udgivet - 7 aug. 2017
Begivenhed	40th International ACM SIGIR Conference on Research and Development in Information Retrieval: SIGIR '17 - Shinjuku, Tokyo, Japan Varighed: 7 aug. 2017 → 11 aug. 2017

Konference

Konference	40th International ACM SIGIR Conference on Research and Development in Information Retrieval
Land/Område	Japan
By	Shinjuku, Tokyo
Periode	07/08/2017 → 11/08/2017

Adgang til dokumentet

10.1145/3077136.3080760

Citationsformater

Seasonal Web Search Query Selection for Influenza-Like Illness (ILI) Estimation. / Hansen, Niels Dalum; Mølbak, Kåre; Cox, Ingemar Johansson et al.
SIGIR '17 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, 2017. s. 1197-1200.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Hansen, ND, Mølbak, K, Cox, IJ & Lioma, C 2017, Seasonal Web Search Query Selection for Influenza-Like Illness (ILI) Estimation. i SIGIR '17 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, s. 1197-1200, 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, 07/08/2017. https://doi.org/10.1145/3077136.3080760

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abstract = "Influenza-like illness (ILI) estimation from web search data is an importantweb analytics task. The basic idea is to use the frequencies of queries in web search logs that are correlated with past ILI activity as features when estimating current ILI activity. It has been noted that since influenza is seasonal, this approach can lead to spurious correlations with features/queries that also exhibit seasonality, but have no relationship with ILI. Spurious correlations can, in turn, degrade performance. To address this issue, we propose modeling the seasonal variation in ILI activity and selecting queries that are correlated with the residual of the seasonal model and the observed ILI signal. Experimental results show that re-ranking queries obtained by Google Correlate based on their correlation with the residual strongly favours ILI-related queries.",

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