Skip to main navigationSkip to main content
The University of Southampton
Web Science Institute

Obesity and Health over Social Networks

Obesity has manifested itself into an epidemic over the past 30 years and is now a major contributor to the global burden of disease (WHO (2000)) and cost (Knai et al. (2007)). At the same time, there is limited information as to the spread patterns of obesity (Ejima et al (2013)). Past studies have highlighted the role of peers and social networks, with Blanchflower et al. (2009) suggesting taking seriously the possibility of socially contagious obesity. Similarly, Christakis et al. (2007) quantitatively model the nature and the extent of the person-to-person spread of obesity and find that networks significantly influence the biologic and behavioural traits of obesity, which appears to spread through social ties.

Given health behaviours spread through networks, a natural question is whether such patterns can be identified through web-based social media like Facebook or Twitter. This work aims at repositioning questions related to environment, behavior and publichealth on social network platforms, such as Twitter. Language traits collected from Twitter have recently been proved to affect significantly economic behaviour (Chen (2013)) and are also shown to improve predictive accuracy of demographic statistics (Culotta (2014)).

We aim at implementing similar protocols to pursue the following main objectives:

1.to embed Twitter derived information into traditional models in order to improve their predictive power;

2.to design health policies that exploit theexplicit network connections, by identifying key players, so that resources invested in health care policy can be efficiently targeted;

3.to characterize the way in which obesity spreads over the network, by modeling two counteracting effects: a behavioural effect (anchoring and self-enforcing lifestyle) that leads to contagion and a risk-sharing effect (by which the network provides support).

Data necessary is being gathered from the Twitter component of Datasift and by other independently programmed sourcesthat have produced protocols that derive relevant health outcomes, information on sentiment metadata, location, and demographics.

REFERENCES:

Blanchflower, D.G. et al. (2009), Imitative obesity and relative utility" Journal of the EEA .

Chen M.K. (2013), The Effect of Languages on Economic Behavior, American Economic Review;

Christakis N.A. et al. (2007), The Spread of Obesity in Large Networks over 32 years, NE Journal of Medicine;

Culotta A. (2014), Estimating County Health Statistics with Twitter, mimeo, Illinois Inst. Of Technology;

Ejima K. et al. (2013), Modeling the obesity epidemic, Theoretical Biology and Medical Modeling;

Knai C. et al. (2007), Obesity in Eastern Europe, Economics and Human Biology;

WHO (2000), Obesity: preventing and managing the global epidemic. Report of a WHO Consultation.

Useful Downloads

Need the software?PDF Reader

Principal Investigator: Dr Antonella Ianni, Economics, Social Sciences

Co-Investigator: Dr Markus Brede, Electronics and Computer Science

Co-Investigator: Dr Emmanouil Mentzakis, Economics, Social Sciences

 

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×