Leveraging social context for searching social media
Proceedings of the 2008 ACM workshop on Search in social media
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Outtweeting the twitterers - predicting information cascades in microblogs
WOSN'10 Proceedings of the 3rd conference on Online social networks
Cross-media impact on twitter in japan
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Capturing users' buying activity at Akihabara electric town from twitter
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Self-supervised capturing of users' activities from weblogs
International Journal of Intelligent Information and Database Systems
Twitter me: using micro-blogging to motivate teenagers to exercise
DESRIST'10 Proceedings of the 5th international conference on Global Perspectives on Design Science Research
Journal of Information Science
Measuring and visualizing interest similarity between microblog users
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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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.