InfoFlow: Mining Information Flow Based on User Community in Social Networking Services

Abstract

Online social networking services (SNSs) have emerged rapidly and have become huge data sources for social network analysis. The spread of the content generated by users is crucial in SNS, but there is only a handful of research works on information diffusion and, more precisely, information diffusion flow. In this paper, we propose a novel method to discover information diffusion processes from SNS data. The method starts preprocessing the SNS data using a user-centric algorithm of community detection based on modularity maximization with the purpose of reducing the complexity of the noisy data. After that, the InfoFlow miner generates information diffusion flow models among the user communities discovered from the data. The algorithm is an extension of a traditional process discovery technique called the Flexible Heuristics miner, but the visualization ability of the generated process model is improved with a new measure called response weight, which effectively captures and represents the interactions among communities. An experiment with Facebook data was conducted, and information flow among user communities was visualized. Additionally, a quality assessment of the models was carried out to demonstrate the effectiveness of the method. The final constructed models allowed us to identify useful information such as how the information flows between communities and information disseminators and receptors within communities.