Paper Info
Title
Fast, Scalable, and Context-Sensitive Detection of Trending Topics in Microblog Post Streams
Abstract
Social networks, such as Twitter, can quickly and broadly disseminate news and memes across both real-world 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 high-velocity 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 Twitter-scale text streams.
Year
DOI
Venue
2013
10.1145/2407740.2407743
ACM Trans. Management Inf. Syst.
Keywords
Field
DocType
formidable problem,consumer interest,high-rate twitter-scale text stream,cultural trend,high-velocity real-time stream,microblog post streams,trending topics,context-sensitive detection,age-old query,real time,best source,incredible volume,microblogs,scalability
World Wide Web,Social network,Social media,Computer science,Microblogging,Dissemination,Scalability
Journal
Volume
Issue
ISSN
3
4
2158-656X
Citations 
PageRank 
References 
14
0.60
14
Authors
5
Name
Order
Citations
PageRank
Nargis Pervin1373.93
Fang Fang2180.99
Anindya Datta3842127.21
Kaushik Dutta456746.90
Debra Vandermeer526518.21