Emotion aware clustering analysis as a tool for Web 2.0 communities detection: Implications for curriculum development

The emergent Web 2.0 reality has advanced a new role for Web users since they now approach information in a dynamic way regulating content, opinions, and policies. Revealing,analyzing and exploiting non-evident (often hidden) communities formulated in Web social networks is crucial, since communities influence content distribution and drive Web trendsand events. It is now important to overcome typical single- criterion community detection methodologies (usually originating from graph mining), and within multidisciplinary efforts advance novel multiple criteria approaches which will identify communities of high coherence and homogeneity. In constructing such Web community indices (both now and in the future Web context) it isvital to consider human behavioral and cognitive criteria, since, it is those that affect users’ activities, preferences and social interactions on the Web. We therefore argue, that within typical processing criteria (such as frequency of access, user profiling, and contentsemantics), we need to incorporate affective criteria which are closely connected to users’ actions and social interactions. In this paper we present an emotion aware clustering approachthat incorporates affect as a central component. This approach can be applied to a range of activities such as: highlighting non-obvious and evolving phenomena on the Web, improvingdata accessing performance, assisting the design of novel content promotion strategies, and developing targeted actions of personalized recommendation. The report identifies thescientific and technical background needed for such multidisciplinary approach on the Web 2.0 and highlights the major topics required for a competitive Web science curriculum.