Social Networks: Predicting and Modeling Tipping Points
http://advances.sciencemag.org/content/2/4/e1501158.full
This paper discusses how information can be diffused through social networks and what causes the tipping point that leads to a cascade. It looks at how social media provides an opportunity to look at what causes social phenomena. Specifically, the authors of the paper “consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence”. They note the difference in social networks in which there exists a tipping point that caused the event to go global. One of the purposes of the study was to identify the tipping point. They build from the methodology of symbolic transfer entropy to determine who in a social network is leading and who is following. They looked at five different events, and discovered that time is an important variable, and it is not constant. In fact, it is elastic. Their model is a first step into predicting tipping points before they occur.
This paper directly relates to the concepts we have covered in class of tipping points and cascades in social networks. In class, we talked about how tipping points can lead to cascades, which the paper examines closely as it tries to model the effects and causes of tipping points in social networks. Furthermore, the paper relates to the topic of population models in general and it relates to structural models as they both involve how, why, and the effect of information flow through a social network.