Please use this identifier to cite or link to this item: https://doi.org/10.1145/2407740.2407743
Title: Fast, scalable, and context-sensitive detection of trending topics in microblog post streams
Authors: Pervin, N.
Fang, F.
Datta, A. 
Dutta, K.
Vandermeer, D.
Keywords: Microblogs
Scalability
Trending topics
Twitter
Issue Date: 2013
Source: Pervin, N.,Fang, F.,Datta, A.,Dutta, K.,Vandermeer, D. (2013). Fast, scalable, and context-sensitive detection of trending topics in microblog post streams. ACM Transactions on Management Information Systems 3 (4). ScholarBank@NUS Repository. https://doi.org/10.1145/2407740.2407743
Abstract: Social networks, such as Twitter, can quickly and broadly disseminate news and memes across both realworld events and cultural trends. Such networks are often the best sources of up-to-the-minute information, and are therefore of considerable commercial and consumer interest. The trending topics that appear first on these networks represent an answer to the age-old query "what are people talking about?" Given the incredible volume of posts (on the order of 45,000 or more per minute), and the vast number of stories about which users are posting at any given time, it is a formidable problem to extract trending stories in real time. In this article, we describe a method and implementation for extracting trending topics from a highvelocity real-time stream of microblog posts. We describe our approach and implementation, and a set of experimental results that show that our system can accurately find "hot" stories from high-rate Twitterscale text streams. © 2013 ACM.
Source Title: ACM Transactions on Management Information Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/42535
ISSN: 2158656X
DOI: 10.1145/2407740.2407743
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