Cargando…

Net-TF-SW: Event Popularity Quantification with Network Structure

Event popularity quantification is essential in the determination of current trends in events on social media and the internet. Particularly, it is important during a crisis to ensure appropriate information transmission and prevention of false-rumor diffusion. Here, we propose Net-TF-SW - a noise-r...

Descripción completa

Detalles Bibliográficos
Autores principales: Nagaya, Hiroshi, Hayashi, Teruaki, Ohsawa, Yukio, Toriumi, Fujio, Torii, Hiroyuki A., Uno, Kazuko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Published by Elsevier B.V. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531921/
https://www.ncbi.nlm.nih.gov/pubmed/33042302
http://dx.doi.org/10.1016/j.procs.2020.09.194
_version_ 1783589825865056256
author Nagaya, Hiroshi
Hayashi, Teruaki
Ohsawa, Yukio
Toriumi, Fujio
Torii, Hiroyuki A.
Uno, Kazuko
author_facet Nagaya, Hiroshi
Hayashi, Teruaki
Ohsawa, Yukio
Toriumi, Fujio
Torii, Hiroyuki A.
Uno, Kazuko
author_sort Nagaya, Hiroshi
collection PubMed
description Event popularity quantification is essential in the determination of current trends in events on social media and the internet. Particularly, it is important during a crisis to ensure appropriate information transmission and prevention of false-rumor diffusion. Here, we propose Net-TF-SW - a noise-robust and explainable topic popularity analysis method. This method is applied to tweets related to COVID-19 and the Fukushima Daiichi Nuclear Disaster, which are two significant crises that have caused significant anxiety and confusion among Japanese citizens. The proposed method is compared to existing methods, and it is verified to be more robust with respect to noise.
format Online
Article
Text
id pubmed-7531921
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Published by Elsevier B.V.
record_format MEDLINE/PubMed
spelling pubmed-75319212020-10-05 Net-TF-SW: Event Popularity Quantification with Network Structure Nagaya, Hiroshi Hayashi, Teruaki Ohsawa, Yukio Toriumi, Fujio Torii, Hiroyuki A. Uno, Kazuko Procedia Comput Sci Article Event popularity quantification is essential in the determination of current trends in events on social media and the internet. Particularly, it is important during a crisis to ensure appropriate information transmission and prevention of false-rumor diffusion. Here, we propose Net-TF-SW - a noise-robust and explainable topic popularity analysis method. This method is applied to tweets related to COVID-19 and the Fukushima Daiichi Nuclear Disaster, which are two significant crises that have caused significant anxiety and confusion among Japanese citizens. The proposed method is compared to existing methods, and it is verified to be more robust with respect to noise. Published by Elsevier B.V. 2020 2020-10-02 /pmc/articles/PMC7531921/ /pubmed/33042302 http://dx.doi.org/10.1016/j.procs.2020.09.194 Text en © 2020 Published by Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Nagaya, Hiroshi
Hayashi, Teruaki
Ohsawa, Yukio
Toriumi, Fujio
Torii, Hiroyuki A.
Uno, Kazuko
Net-TF-SW: Event Popularity Quantification with Network Structure
title Net-TF-SW: Event Popularity Quantification with Network Structure
title_full Net-TF-SW: Event Popularity Quantification with Network Structure
title_fullStr Net-TF-SW: Event Popularity Quantification with Network Structure
title_full_unstemmed Net-TF-SW: Event Popularity Quantification with Network Structure
title_short Net-TF-SW: Event Popularity Quantification with Network Structure
title_sort net-tf-sw: event popularity quantification with network structure
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531921/
https://www.ncbi.nlm.nih.gov/pubmed/33042302
http://dx.doi.org/10.1016/j.procs.2020.09.194
work_keys_str_mv AT nagayahiroshi nettfsweventpopularityquantificationwithnetworkstructure
AT hayashiteruaki nettfsweventpopularityquantificationwithnetworkstructure
AT ohsawayukio nettfsweventpopularityquantificationwithnetworkstructure
AT toriumifujio nettfsweventpopularityquantificationwithnetworkstructure
AT toriihiroyukia nettfsweventpopularityquantificationwithnetworkstructure
AT unokazuko nettfsweventpopularityquantificationwithnetworkstructure