Cargando…
Quantifying Collective Attention from Tweet Stream
Online social media are increasingly facilitating our social interactions, thereby making available a massive “digital fossil” of human behavior. Discovering and quantifying distinct patterns using these data is important for studying social behavior, although the rapid time-variant nature and large...
Autores principales: | Sasahara, Kazutoshi, Hirata, Yoshito, Toyoda, Masashi, Kitsuregawa, Masaru, Aihara, Kazuyuki |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3640043/ https://www.ncbi.nlm.nih.gov/pubmed/23637913 http://dx.doi.org/10.1371/journal.pone.0061823 |
Ejemplares similares
-
Correction: Quantifying Collective Attention from Tweet
Stream
por: Sasahara, Kazutoshi, et al.
Publicado: (2013) -
Tracking Time Evolution of Collective Attention Clusters in Twitter: Time Evolving Nonnegative Matrix Factorisation
por: Saito, Shota, et al.
Publicado: (2015) -
Image Analysis Enhanced Event Detection from Geo-Tagged Tweet Streams
por: Han, Yi, et al.
Publicado: (2020) -
Forecasting high-dimensional dynamics exploiting suboptimal embeddings
por: Okuno, Shunya, et al.
Publicado: (2020) -
Forecasting large aftershocks within one day after the main shock
por: Omi, Takahiro, et al.
Publicado: (2013)