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Several studies have been developed over the years in the areas of Text Mining, Social Network Analysis and Detection of Communities. In this paper, we present a new technique for community detection in social networking using the conversation of users in a social network. We showed that the proposal performed very well for a specific theme for defining a community and performed well for joint themes...
Conventional online social networks (OSNs) are implemented in a centralized manner. Although centralization is a convenient way for implementing OSNs, it has several well known drawbacks. Chief among them are the risks they pose to the security and privacy of the information maintained by the OSN; and the loss of control over the information contributed by individual members.
In this paper, we present a novel method for deep user profiling using information available on social networks about a user's activities and interests. We use this information to personalize the search for the user and make results available via web and low end mobile phones. In addition to using a user's profile as a context, we also use the context of location and implement a location based search...
In this paper we present a fully automatic system for face augmentation on mobile devices. A user can point his mobile phone to a person and the system recognizes his or her face. A tracking algorithm overlays information about the identified person on the screen, thereby achieving an augmented reality effect. The tracker is running on the mobile client, while the recognition is running on a server...
This paper describes a convenient method of processing and contextualizing information extracted from social networking systems such as Myspace, Facebook or Hi5 users in real-time by using the Yahoo! Pipes feed mash-up service and formal concept analysis. Interests referring to media consumption (favorite movies, favorite music, favorite books or role models) declared by users can be expanded into...
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