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

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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

Inuenza-like illness (ILI) estimation from web search data is an important
web 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 inuenza 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.
OriginalsprogEngelsk
TitelSIGIR '17 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
ForlagAssociation for Computing Machinery
Publikationsdato2017
Sider1197-1200
ISBN (Elektronisk)978-1-4503-5022
DOI
StatusUdgivet - 2017
Begivenhed40th International ACM SIGIR Conference on Research and Development in Information Retrieval: SIGIR '17 - Shinjuku, Tokyo, Japan
Varighed: 7 aug. 201711 aug. 2017

Konference

Konference40th International ACM SIGIR Conference on Research and Development in Information Retrieval
LandJapan
ByShinjuku, Tokyo
Periode07/08/201711/08/2017

ID: 195769168