A game theoretic analysis of Engagement in Social Networks
Gore, Shounak Uday
MetadataShow full item record
A variety of models have been proposed to study the diffusion of ideas or influence among individuals in a social network. These models make use of various concepts such as centrality, information propagation over paths, stochastic simulation of information propagation scenarios and natural language processing-based tools to quantify the amount of “influence” network actors have on each other. However, many of those concepts are heuristic in that their definitions do not accurately reflect the interpretable meaning of “influence” as the capacity to have an effect on the character, development or behavior of someone or something. Various ways to measure influence have been shown to be useful for special case applications but they cannot be generalized to suit all applications. Offering a new perspective on explaining and measuring influence, this thesis argues that the users of an online resource can be seen as creating synergistic value, which is a function of their communication and order. This value can be viewed as engagement capacity of the online resource, with different users contributing differently towards this engagement capacity. This thesis lays a theoretical foundation for measuring engagement as a driver of reach that achieves growth via positive externality effects. The thesis takes a game theoretic approach to quantifying engagement, viewing a platform's social support capital as a cooperatively created value and finding a fair distribution of this value among the contributors. It introduces engagement capacity, a measure of the ability of users and user groups to engage peers, and formulates the Engaging Team Formation Problem (EngTFP) to identify sets of users that "make the platform go". We distinguish our analyses, which underlie the reach maximization efforts, from the pre-existing influence maximization work and compare the engagement capacity with network-based metrics. Computational investigations with MedHelp and Twitter data reveal the properties of engagement capacity and the utility of EngTFP.