The scale and complexity of the traces of human behavior captured on the web
has been a useful resource for tracking disease, pushing the lag of
conventional health tracking to more real-time "now-casting". These signals
provide a rich source of information about the context of people's health
conditions, revealing their cultural, social, and personal attitudes and
behaviors, making social media data especially useful for understanding
lifestyle diseases, such as obesity and diabetes -- conditions that claim
more lives than infectious diseases worldwide. This talk will discuss the
latest findings in lifestyle disease tracking via large social media
collections encompassing population and individual scales.
*Bio*: Yelena Mejova is a Research Leader at the ISI Foundation in Turin,
Italy, a part of the Digital Epidemiology group (previously, a scientist at
the Qatar Computing Research Institute, Qatar). Specializing in social media
analysis and mining, her work concerns the quantification of health and
wellbeing signals in social media, as well as tracking of social phenomena,
including politics and news consumption. She co-edited a volume on the use of
Twitter for social science in "Twitter: A Digital Socioscope", and a special
issue of Social Science Computer Review on Quantifying Politics Using Online
Data. Previously, as a postdoctoral member of the Web Mining team at Yahoo!
Research Barcelona, Yelena participated in the Linguistically Motivated
Semantic Aggregation Engines (LiMoSINe) EU project. Also, Yelena has
published widely on sentiment analysis and its application to social media
and political speech.
13/10/2018