Abstract
A high percentage of information that propagates through a social network is sourced from different exogenous sources. E.g., individuals may form their opinions about products based on their own experience or reading a product review, and then share that with their social network. This sharing then diffuses through the network, evolving as a combination of both network and external effects. Besides, different individuals (nodes in a social network) have different degrees of exposition to their external sources, as well. Modeling this influence of external sources is important in order to understand the diffusion process and predict future content sharing patterns. Recognizing this fusion of intrinsic (network) effect and exogenous (external) effect, this paper develops a novel fuzzy relative willingness (FRW) model. Leveraging a fuzzy set approach provides a way to handle the uncertainties arising within the human concept of willingness. We demonstrate that FRW is able to accurately identify both top- $k$ most content producers and diffusion effect based on external influence. We also demonstrate that the fuzzy set theory provides a compelling framework to model uncertainties pertaining to the influence as well as the susceptibility of individuals for both network and exogenous effects.
Highlights
Quantifying how much a person is willing to accept information from external sources is an interesting problem in the context of information diffusion
We use a fuzzy set to express the uncertainties and propose a fuzzy relative willingness (FRW) measure, which characterizes the relative willingness of a node to adopt from exogenous factors
We evaluate efficacy of FRW to predict the future states on a real-world data set: a Twitter social graph and hash tag data (Table 5)
Summary
Quantifying how much a person is willing to accept information from external sources is an interesting problem in the context of information diffusion. We use a fuzzy set to express the uncertainties and propose a fuzzy relative willingness (FRW) measure, which characterizes the relative willingness of a node to adopt from exogenous factors. Comparative results on two real-world networks show that selecting topk nodes based on proposed FRW measure outperforms other baseline algorithms in predicting the number of externally influenced future content shares. The contribution of the paper is as follows: 1) We define a new problem of quantifying willingness to adopt and propagate from exogenous influence. 3) we use the FRW measure in a newly defined ranking problem, which maximizes the number of externally influenced future content shares in the network.
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