Cargando…
Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19
In the era of advanced mobile technology, freedom of expression over social media has become prevalent among online users. This generates a huge amount of communication that eventually forms a ground for extensive research and analysis. The social network analysis allows identifying the influential...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305915/ https://www.ncbi.nlm.nih.gov/pubmed/32834597 http://dx.doi.org/10.1016/j.chaos.2020.110037 |
_version_ | 1783548565021261824 |
---|---|
author | Jain, Somya Sinha, Adwitiya |
author_facet | Jain, Somya Sinha, Adwitiya |
author_sort | Jain, Somya |
collection | PubMed |
description | In the era of advanced mobile technology, freedom of expression over social media has become prevalent among online users. This generates a huge amount of communication that eventually forms a ground for extensive research and analysis. The social network analysis allows identifying the influential people in society over microblogging platforms. Twitter, being an evolving social media platform, has become increasingly vital for online dialogues, trends, and content virality. Applications of discovering influential users over Twitter are manifold. It includes viral marketing, brand analysis, news dissemination, health awareness spreading, propagating political movement, and opinion leaders for empowering governance. In our research, we have proposed a sustainable approach, namely Weighted Correlated Influence (WCI), which incorporates the relative impact of timeline-based and trend-specific features of online users. Our methodology considers merging the profile activity and underlying network topology to designate online users with an influence score, which represents the combined effect. To quantify the performance of our proposed method, the Twitter trend #CoronavirusPandemic is used. Also, the results are validated for another social media trend. The experimental outcomes depict enhanced performance of proposed WCI over existing methods that are based on precision, recall, and F1-measure for validation. |
format | Online Article Text |
id | pubmed-7305915 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73059152020-06-22 Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19 Jain, Somya Sinha, Adwitiya Chaos Solitons Fractals Article In the era of advanced mobile technology, freedom of expression over social media has become prevalent among online users. This generates a huge amount of communication that eventually forms a ground for extensive research and analysis. The social network analysis allows identifying the influential people in society over microblogging platforms. Twitter, being an evolving social media platform, has become increasingly vital for online dialogues, trends, and content virality. Applications of discovering influential users over Twitter are manifold. It includes viral marketing, brand analysis, news dissemination, health awareness spreading, propagating political movement, and opinion leaders for empowering governance. In our research, we have proposed a sustainable approach, namely Weighted Correlated Influence (WCI), which incorporates the relative impact of timeline-based and trend-specific features of online users. Our methodology considers merging the profile activity and underlying network topology to designate online users with an influence score, which represents the combined effect. To quantify the performance of our proposed method, the Twitter trend #CoronavirusPandemic is used. Also, the results are validated for another social media trend. The experimental outcomes depict enhanced performance of proposed WCI over existing methods that are based on precision, recall, and F1-measure for validation. Elsevier Ltd. 2020-10 2020-06-20 /pmc/articles/PMC7305915/ /pubmed/32834597 http://dx.doi.org/10.1016/j.chaos.2020.110037 Text en © 2020 Elsevier Ltd. All rights reserved. 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 Jain, Somya Sinha, Adwitiya Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19 |
title | Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19 |
title_full | Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19 |
title_fullStr | Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19 |
title_full_unstemmed | Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19 |
title_short | Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19 |
title_sort | identification of influential users on twitter: a novel weighted correlated influence measure for covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305915/ https://www.ncbi.nlm.nih.gov/pubmed/32834597 http://dx.doi.org/10.1016/j.chaos.2020.110037 |
work_keys_str_mv | AT jainsomya identificationofinfluentialusersontwitteranovelweightedcorrelatedinfluencemeasureforcovid19 AT sinhaadwitiya identificationofinfluentialusersontwitteranovelweightedcorrelatedinfluencemeasureforcovid19 |