  Influenza, an infectious disease, causes many deaths worldwide. Predicting
influenza victims during epidemics is an important task for clinical, hospital,
and community outbreak preparation. On-line user-generated contents (UGC),
primarily in the form of social media posts or search query logs, are generally
used for prediction for reaction to sudden and unusual outbreaks. However, most
studies rely only on the UGC as their resource and do not use various UGCs. Our
study aims to solve these questions about Influenza prediction: Which model is
the best? What combination of multiple UGCs works well? What is the nature of
each UGC? We adapt some models, LASSO Regression, Huber Regression, Support
Vector Machine regression with Linear kernel (SVR) and Random Forest, to test
the influenza volume prediction in Japan during 2015 - 2018. For that, we use
on-line five data resources: (1) past flu patients, (2) SNS (Twitter), (3)
search engines (Yahoo! Japan), (4) shopping services (Yahoo! Shopping), and (5)
Q&A services (Yahoo! Chiebukuro) as resources of each model. We then validate
respective resources contributions using the best model, Huber Regression, with
all resources except one resource. Finally, we use Bayesian change point method
for ascertaining whether the trend of time series on any resources is reflected
in the trend of flu patient count or not. Our experiments show Huber Regression
model based on various data resources produces the most accurate results. Then,
from the change point analysis, we get the result that search query logs and
social media posts for three years represents these resources as a good
predictor. Conclusions: We show that Huber Regression based on various data
resources is strong for outliers and is suitable for the flu prediction.
Additionally, we indicate the characteristics of each resource for the flu
prediction.
