Applications of approximate word matching in information retrieval
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Inverted files for text search engines
ACM Computing Surveys (CSUR)
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Proceedings of the first workshop on Online social networks
How and why people Twitter: the role that micro-blogging plays in informal communication at work
Proceedings of the ACM 2009 international conference on Supporting group work
Characterizing debate performance via aggregated twitter sentiment
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Monitoring geo-social activities through micro-blogging sites
DASFAA'10 Proceedings of the 15th international conference on Database systems for advanced applications
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Urban area characterization based on crowd behavioral lifelogs over Twitter
Personal and Ubiquitous Computing
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Due to the advance of many social networking sites, social analytics by aggregating and analyzing crowds' life logs are attracting a great deal of attention. In the meantime, there is an interesting trend that people watching TVs are also writing Twitter messages pertaining to their opinions. With the utilization of bigger and broader crowds over Twitter, surveying massive audiences' lifestyles will be an important aspect of exploitation of crowd-sourced data. In this paper, for better TV viewing rates in the light of the evolving TV lifestyles beyond home environments, we propose a TV rating method by means of Twitter where we can easily find crowd voices relative to TV watching. In the experiment, we describe our exploratory survey to exploit a large amount of Twitter messages to populate TV programs and on-line video sites.