eprintid: 1909
rev_number: 13
eprint_status: archive
userid: 43
dir: disk0/00/00/19/09
datestamp: 2013-11-13 14:40:45
lastmod: 2013-11-13 15:01:45
status_changed: 2013-11-13 14:40:45
type: book_section
metadata_visibility: show
creators_name: Tolomei, Gabriele
creators_name: Orlando, Salvatore
creators_name: Ceccarelli, Diego
creators_name: Lucchese, Claudio
creators_id: gabriele.tolomei@isti.cnr.it
creators_id: salvatore.orlando@isti.cnr.it
creators_id: diego.ceccarelli@imtlucca.it
creators_id: claudio.lucchese@isti.cnr.it
title: Twitter anticipates bursts of requests for Wikipedia articles
ispublished: pub
subjects: QA75
subjects: QA76
divisions: EIC
full_text_status: public
abstract: 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.
date: 2013-10-28
publisher: ACM
pagerange: 5-8
pages: 4
refereed: TRUE
isbn: 978-1-4503-2417-5 
book_title:  Proceedings of the 2013 workshop on Data-driven user behavioral modelling and mining from social media
official_url: http://dl.acm.org/citation.cfm?id=2538768&CFID=378643557&CFTOKEN=76519872
related_url_url: http://www.dxtr.it
related_url_url: http://dexter.isti.cnr.it
related_url_type: author
related_url_type: author
citation:   Tolomei, Gabriele and Orlando, Salvatore and Ceccarelli, Diego and Lucchese, Claudio  Twitter anticipates bursts of requests for Wikipedia articles.   In:   Proceedings of the 2013 workshop on Data-driven user behavioral modelling and mining from social media.   ACM, pp. 5-8.  ISBN 978-1-4503-2417-5       (2013)  
document_url: http://eprints.imtlucca.it/1909/1/dubmod03-tolomei.pdf