TY  - CHAP
ID  - eprints1909
EP  - 8
T2  -  Proceedings of the 2013 workshop on Data-driven user behavioral modelling and mining from social media
Y1  - 2013/10/28/
AV  - public
TI  - Twitter anticipates bursts of requests for Wikipedia articles
UR  - http://dl.acm.org/citation.cfm?id=2538768&CFID=378643557&CFTOKEN=76519872
SN  - 978-1-4503-2417-5 
PB  - ACM
A1  - Tolomei, Gabriele
A1  - Orlando, Salvatore
A1  - Ceccarelli, Diego
A1  - Lucchese, Claudio
N2  - Most of the tweets that users exchange on Twitter make implicit mentions of named-entities, which in turn can be mapped to corresponding Wikipedia articles using proper Entity Linking (EL) techniques. Some of those become trending entities on Twitter due to a long-lasting or a sudden effect on the volume of tweets where they are mentioned. We argue that the set of trending entities discovered from Twitter may help predict the volume of requests for relating Wikipedia articles. To validate this claim, we apply an EL technique to extract trending entities from a large dataset of public tweets. Then, we analyze the time series derived from the hourly trending score (i.e., an index of popularity) of each entity as measured by Twitter and Wikipedia, respectively. Our results reveals that Twitter actually leads Wikipedia by one or more hours.
SP  - 5
ER  -