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Citizen Sensing - Mining Social Signals & Perceptions: Microsoft Research Faculty Summit

Title Citizen Sensing - Mining Social Signals & Perceptions: Microsoft Research Faculty Summit
Venue Microsoft Research Faculty Summit 2011
Location Redmond, WA.
Year 2011
Resource Type Invited Talk
Keyword(s) Twitris,semantic web,semantic sensor web,continuous semantics,dynamic domain model,Twarql,Taalee,semantic perception,citizen sensing,dynamic evolving model,real-time social data analysis,machine sensing,spatio-temporal-themic processing of social data,people-content-network analysis of social data,user-community engagement,semantic abstraction
Full Citation Amit Sheth, 'Citizen Sensing-Opportunities and Challenges in Mining Social Signals and Perceptions' Invited Talk at Microsoft Research Faculty Summit 2011, Redmond, WA, July 19, 2011.
Abstract Millions of persons have become 'citizens' of an Internet- or Web-enabled social community. Web 2.0 fostered the open environment and applications for tagging, blogging, wikis, and social networking sites that have made information consumption, production, and sharing so incredibly easy. An interconnected network of people who actively observe, report, collect, analyze, and disseminate information via text, audio, or video messages, increasingly through pervasively connected mobile devices, has led to what we term citizen sensing. In this talk, we review recent progress in supporting collective intelligence through intelligent processing of citizen sensing. Key issues we cover in this talk are: - even-specific analysis of citizen sensing and discuss opportunities and challenges in understanding temporal, spatial and thematic cues - facets of people-content-network analysis with focus on user-community engagement analysis - real time social media data analysis, and the concept of continuos semantics supported by dynamic model creation - opportunity in integrated analysis of citizen sensing and machine sensing data, and the recent advance in developing semantic abstracts or semantic perception to convert massive amounts of raw observational data into nuggets of information and insights that can aid in human decision making. Throughout we investigate the role and benefits of using semantic approach, especially by contextually applying relevant background knowledge, and demonstrate examples on real-world data using systems developed at Kno.e,sis, including Twitris, Twarql and Semantic Sensor Web.
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