SpringerOpen Newsletter

Receive periodic news and updates relating to SpringerOpen.

Open Access Highly Accessed Regular article

The dynamics of health behavior sentiments on a large online social network

Marcel Salathé123*, Duy Q Vu4, Shashank Khandelwal12 and David R Hunter14

Author Affiliations

1 Center for Infectious Disease Dynamics, Penn State University, University Park, PA, USA

2 Department of Biology, Penn State University, University Park, PA, USA

3 Department of Computer Sciences and Engineering, Penn State University, University Park, PA, USA

4 Department of Statistics, Penn State University, University Park, PA, USA

For all author emails, please log on.

EPJ Data Science 2013, 2:4  doi:10.1140/epjds16

Published: 4 April 2013

Abstract

Modifiable health behaviors, a leading cause of illness and death in many countries, are often driven by individual beliefs and sentiments about health and disease. Individual behaviors affecting health outcomes are increasingly modulated by social networks, for example through the associations of like-minded individuals - homophily - or through peer influence effects. Using a statistical approach to measure the individual temporal effects of a large number of variables pertaining to social network statistics, we investigate the spread of a health sentiment towards a new vaccine on Twitter, a large online social network. We find that the effects of neighborhood size and exposure intensity are qualitatively very different depending on the type of sentiment. Generally, we find that larger numbers of opinionated neighbors inhibit the expression of sentiments. We also find that exposure to negative sentiment is contagious - by which we merely mean predictive of future negative sentiment expression - while exposure to positive sentiments is generally not. In fact, exposure to positive sentiments can even predict increased negative sentiment expression. Our results suggest that the effects of peer influence and social contagion on the dynamics of behavioral spread on social networks are strongly content-dependent.

Keywords:
social media; social network; diffusion; health behavior; contagion