More and more people today are using activity trackers like Fitbit and connecting them to their social media like Twitter. And what more, some people make their daily quantified activity time series public. So on one hand, we have their entire Twitter network in the form of timelines, friends, followers and their entire network and on the other hand, we have their entire workout data. We are trying to answer some interesting question from these two sets of data. What is the effect of social network on one health regime? Is there any correlation between the number of times a person posts about health and the average workout the person does. Previous research has shown that having friends who are also health conscious actually increases one's tendency to adhere to health regimes. We are trying answer how does weak and strong ties to health conscious and non-health conscious friends and followers affect a person's adherence to health regimens. Previous research shows support in the form of retweets, comments, loves of a users' health tweet actually motivates him to continue using quantified health devices, but now with the exact data of a person's workout, we are trying to quantify this motivation.
The SocWeB Lab's mission is to develop novel computational techniques, and technologies powered by these techniques, to responsibly and ethically employ social media in quantifying, understanding, and improving our mental health and well-being.