English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

Comunicación presentada en la 21st international conference on World Wide Web, celebrada en Lyon, del 16 al 20 de abril de 2012.-- Versión Pre-printMicro-blogging systems such as Twitter expose digital traces of social discourse with an unprecedented degree of resolution of individual behaviors. They offer an opportunity to investigate how a large-scale social system responds to exogenous or endogenous stimuli, and to disentangle the temporal, spatial and topical aspects of users' activity. Here we focus on spikes of collective attention in Twitter, and specifically on peaks in the popularity of hashtags. Users employ hashtags as a form of social annotation, to define a shared context for a specific event, topic, or meme. We analyze a large-scale record of Twitter activity and find that the evolution of hastag popularity over time defines discrete classes of hashtags. We link these dynamical classes to the events the hashtags represent and use text mining techniques to provide a semantic characterization of the hastag classes. Moreover, we track the propagation of hashtags in the Twitter social network and find that epidemic spreading plays a minor role in hastag popularity, which is mostly driven by exogenous factors.This work was carried out while JL was at the ISI Foundation with support from the Leonardo da Vinci Scholarship. JL acknowledges support from the Spanish Ministry of Science through the project TIN2009- 14560-C03-01. JJR acknowledges support from the JAE program of the CSIC and from the Spanish Ministry of Sci- ence (MICINN) through the project MODASS (FIS2011- 24785). CC acknowledges support from the Lagrange Project funded by the CRT Foundation and from the Q-ARACNE project funded by the Fondazione Compagnia di San Paolo.Peer Reviewe

Dynamical classes of collective attention in twitter