Introduction to topic detection and tracking
Topic detection and tracking
Why we search: visualizing and predicting user behavior
Proceedings of the 16th international conference on World Wide Web
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Predicting the popularity of online content
Communications of the ACM
The wisdom of social multimedia: using flickr for prediction and forecast
Proceedings of the international conference on Multimedia
Traffic in Social Media I: Paths Through Information Networks
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
Hip and trendy: Characterizing emerging trends on Twitter
Journal of the American Society for Information Science and Technology
Modeling and predicting behavioral dynamics on the web
Proceedings of the 21st international conference on World Wide Web
SocialSensor: sensing user generated input for improved media discovery and experience
Proceedings of the 21st international conference companion on World Wide Web
Propagation-based social-aware replication for social video contents
Proceedings of the 20th ACM international conference on Multimedia
SocialTransfer: cross-domain transfer learning from social streams for media applications
Proceedings of the 20th ACM international conference on Multimedia
Dynamic vocabularies for web-based concept detection by trend discovery
Proceedings of the 20th ACM international conference on Multimedia
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Among the vast information available on the web, social media streams capture what people currently pay attention to and how they feel about certain topics. Awareness of such trending topics plays a crucial role in multimedia systems such as trend aware recommendation and automatic vocabulary selection for video concept detection systems. Correctly utilizing trending topics requires a better understanding of their various characteristics in different social media streams. To this end, we present the first comprehensive study across three major online and social media streams, Twitter, Google, and Wikipedia, covering thousands of trending topics during an observation period of an entire year. Our results indicate that depending on one's requirements one does not necessarily have to turn to Twitter for information about current events and that some media streams strongly emphasize content of specific categories. As our second key contribution, we further present a novel approach for the challenging task of forecasting the life cycle of trending topics in the very moment they emerge. Our fully automated approach is based on a nearest neighbor forecasting technique exploiting our assumption that semantically similar topics exhibit similar behavior. We demonstrate on a large-scale dataset of Wikipedia page view statistics that forecasts by the proposed approach are about 9-48k views closer to the actual viewing statistics compared to baseline methods and achieve a mean average percentage error of 45-19% for time periods of up to 14 days.