Research
Real-time Public Concern of Diseases
Public concern may cause unnecessary anxiety and other negative social consequences. Keeping track of trends in public health concerns and identifying peaks of public concern are crucial tasks.
To address these problems, we classified user-generated social network data, i.e. Twitter messages, into different sentiment category. We have also quantified the sentiment trend by defining a measure of concern (MOC) derived from relevant Twitter messages. Using the results of sentiment classification, we compute the MOC in a time interval, the MOC is displayed in real-time to quantify the public concern in both temporal and geographic dimension.
Real-time Monitoring of Disease Outbreaks
To monitor epidemics using social media data, four visualizations are generated.
(1) The instance map is used to show the tweets based on “single” users’ locations.
(2) In the distribution map, absolute and relative frequencies of the distribution are displayed. The relative frequency is the absolute frequency divided by the population of each state. The distribution map enables the detection of which states house most Twitter users tweeting about an epidemic.
(3) The filter map gives users the flexibility to monitor the spread of epidemics based on time series and users’ influence with a (minimum, maximum) range of followers to only display Twitter users in this range.
(4) Hashtag cloud presents a frequency-based word list of disease. By clicking on each hashtag, the related tweets mentioning this hashtag will be shown.
Social InfoButtons
With the Health 3.0 trend, it is increasingly becoming important to understand the patients’ actual health practices, behaviors, trends and concerns. Social InfoButtons system generates contextually summarized information about social health practices by geographic or temporal dimensions, providing end-users (e.g. patients, clinicians, or government officials) with healthcare information, such as treatments, practices, conditions, experiences, sentiments, and behaviors reported by other patients through social media.