A social networking service (SNS) is an online platform for creating relationships with other people who share an interest, background, or real relationship. Social networking service users create a profile with personal information and photos and form connections with other profiles. Social networking services vary in format and the number of features. They can incorporate a range of new information and communication tools, operating on desktops and on laptops, on mobile devices such as tablet computers and smart phones. This may feature digital photo/video/sharing and diary entries online (blogging).Online community services are sometimes considered social-network services by developers and users, though in a broader sense, a social-network service usually provides an individual centered service whereas online community services are groups centered. We propose a novel method to discover information diffusion processes from SNS data. The method starts pre- processing 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 Info Flow 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. 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
Key words: Information flow, social networking services, community detection, network modularity, Process mining.
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