Ingmar Weber - Using Social Media for Health Studies

10:00
Lundi
28
Nov
2016
  • Séminaire
Lieu : 
Intervenant : 
Ingmar Weber
Équipes : 

Ingmar Weber is a senior scientist in the Social Computing group at the Qatar Computing Research Institute (QCRI). His interdisciplinary research uses large amounts of online data from social media and other sources to study human behavior at scale. Particular topics of interest include studying lifestyle diseases and population health, quantifying international migration using digital methods, and looking at political polarization and extremism. He has published over 100 peer-reviewed articles and his work is frequently featured in popular press. Since 2016 he has been selected as an ACM Distinguished Speaker. 

As an undergraduate Dr. Weber studied mathematics at Cambridge University (1999-2003), before pursuing a PhD at the Max-Planck Institute for Computer Science (2003-2007). He subsequently held positions at the Ecole Polytechnique Federale de Lausanne (2007-2009) and Yahoo Research Barcelona (2009-2012), as well as a visiting researcher position at Microsoft Research Cambridge (summer 2008). He serves on a number of program committees for top-tier conferences in the domain of web data mining and social media analysis including ICWSM, KDD, WWW, ACL, SDM, VLDB and WebSci, as well as on the editorial board for the Journal of Web Science.

In this presentation, I’ll present research done at the Qatar Computing Research Institute on using social media for health studies. Most of my work in this domain is related to understanding lifestyle diseases such as obesity and diabetes and has a two-fold goal: (i) monitoring population health and health-related lifestyles, and (ii) combining social media and quantified self data for a more complete and holistic patient view.

Concerning population-level studies, I’ll present results on using food mentions on Twitter (CHI’15) and Instagram images (CHI’16) for modeling regional variations in obesity and diabetes rates. I’ll also discuss a feasibility study on crowdsourcing “does this person look overweight” labels (DigitalHealth’16).

On the combination of quantified self and social media data, I’ll show how tweets from smart scales create an interesting data linking the two domains (DigitalHealth’16), how data from sleeping tracking apps on social media can be used for sleep studies (ICHI’16), and howpublic food diaries can help us predict dieting success or failure (CSCW’17, forthcoming).

If there’s time, then I’ll also discuss the aspect of behavioral change and the importance of receiving social feedback on continued participation in a weight loss subreddit (DigitalHealth’16).