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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier B.V.
2020
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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 |
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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 |
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