  Influenza is an acute respiratory infection caused by a virus. It is highly
contagious and rapidly mutative. However, its epidemiological characteristics
are conventionally collected in terms of outpatient records. In fact, the
subjective bias of the doctor emphasizes exterior signs, and the necessity of
face-to-face inquiry results in an inaccurate and time-consuming manner of data
collection and aggregation. Accordingly, the inferred spectrum of syndromes can
be incomplete and lagged. With a massive number of users being sensors, online
social media can indeed provide an alternative approach. Voluntary reports in
Twitter and its variants can deliver not only exterior signs but also interior
feelings such as emotions. These sophisticated signals can further be
efficiently collected and aggregated in a real-time manner, and a comprehensive
spectrum of syndromes could thus be inferred. Taking Weibo as an example, it is
confirmed that a regional spectrum of symptoms can be credibly sensed. Aside
from the differences in symptoms and treatment incentives between northern and
southern China, it is also surprising that patients in the south are more
optimistic, while those in the north demonstrate more intense emotions. The
differences sensed from Weibo can even help improve the performance of
regressions in monitoring influenza. Our results suggest that self-reports from
social media can be profound supplements to the existing clinic-based systems
for influenza surveillance.
