Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Minimum-effort driven dynamic faceted search in structured databases
Proceedings of the 17th ACM conference on Information and knowledge management
FacetLens: exposing trends and relationships to support sensemaking within faceted datasets
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Twitter under crisis: can we trust what we RT?
Proceedings of the First Workshop on Social Media Analytics
Multi-select faceted navigation based on minimum description length principle
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Exploratory analysis of highly heterogeneous document collections
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Recent years have seen an exponential increase in the number of users of social media sites. As the number of users of these sites continues to grow at an extraordinary rate, the amount of data produced follows in magnitude. With this deluge of social media data, the need for comprehensive tools to analyze user interactions is ever increasing. In this paper, we present a novel tool, Navigating Information Facets on Twitter (NIF-T), which helps users to explore data generated on social media sites. Using the three dimensions or facets: time, location, and topic as an example of the many possible facets, we enable the users to explore large social media datasets. With the help of a large corpus of tweets collected from the Occupy Wall Street movement on the Twitter platform we show how our system can be used to identify important aspects of the event along these facets.