Sonia Soubam, Dipyaman Banerjee, et al.
ICDCN 2016
Recent technological advances in mobile-based access to social networking platforms and facilities to update information in real{time (e.g. in Facebook) have allowed an individual's online presence to be as ephemeral and dynamic in nature, as her very thoughts and interests. In this context, micro-blogging has been widely adopted by users as an effective means to capture and disseminate their thoughts and actions to a larger audience on a daily basis. Interestingly, daily chatters of a user obtained from her micro-blogs offer a unique information source to analyze and interpret her context in real-time - i.e. interests, intentions,and activities. In this paper, we gather data from the public timeline of Twitter spanning across ten worldwide cities over a period of four weeks. We use this dataset to (a) explore how users express interests in real-time through micro-blogs, and (b) understand how text mining techniques can be applied to interpret real-time context of a user based on her tweets. Initial findings reported herein suggest that social media sites like Twitter constitute a promising source for extracting user context that can be exploited by novel social networking applications. Copyright 2009 ACM.
Sonia Soubam, Dipyaman Banerjee, et al.
ICDCN 2016
Asad Sayeed, Soumitra Sarkar, et al.
CIKM 2009
Dipanjan Chakraborty, Hui Lei
PerCom 2004
Arup Acharya, Nilankan Banerjee, et al.
IEEE Pervasive Computing