  In this work, we aim to determine the main factors driving behavioral change
during the seasonal flu. To this end, we analyze a unique dataset comprised of
599 surveys completed by 434 Italian users of Influweb, a Web platform for
participatory surveillance, during the 2017-18 and 2018-19 seasons. The data
provide socio-demographic information, level of concerns about the flu, past
experience with illnesses, and the type of behavioral changes implemented by
each participant. We describe each response with a set of features and divide
them in three target categories. These describe those that report i) no (26 %),
ii) only moderately (36 %), iii) significant (38 %) changes in behaviors. In
these settings, we adopt machine learning algorithms to investigate the extent
to which target variables can be predicted by looking only at the set of
features. Notably, $66\%$ of the samples in the category describing more
significant changes in behaviors are correctly classified through Gradient
Boosted Trees. Furthermore, we investigate the importance of each feature in
the classification task and uncover complex relationships between individuals'
characteristics and their attitude towards behavioral change. We find that
intensity, recency of past illnesses, perceived susceptibility to and perceived
severity of an infection are the most significant features in the
classification task. Interestingly, the last two match the theoretical
constructs suggested by the Health-Belief Model. Overall, the research
contributes to the small set of empirical studies devoted to the data-driven
characterization of behavioral changes induced by infectious diseases.
