Citizen Sensing: Opportunities and Challenges in Mining Social Signals and Perceptions

Document Type



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 continuous 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.

APA Citation

(2011). Citizen Sensing: Opportunities and Challenges in Mining Social Signals and Perceptions.