Cargando…
Developing a socio-computational approach to examine toxicity propagation and regulation in COVID-19 discourse on YouTube
As the novel coronavirus (COVID-19) continues to ravage the world at an unprecedented rate, formal recommendations from medical experts are becoming muffled by the avalanche of toxic content posted on social media platforms. This high level of toxic content prevents the dissemination of important an...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9759669/ https://www.ncbi.nlm.nih.gov/pubmed/36567973 http://dx.doi.org/10.1016/j.ipm.2021.102660 |
_version_ | 1784852283612200960 |
---|---|
author | Obadimu, Adewale Khaund, Tuja Mead, Esther Marcoux, Thomas Agarwal, Nitin |
author_facet | Obadimu, Adewale Khaund, Tuja Mead, Esther Marcoux, Thomas Agarwal, Nitin |
author_sort | Obadimu, Adewale |
collection | PubMed |
description | As the novel coronavirus (COVID-19) continues to ravage the world at an unprecedented rate, formal recommendations from medical experts are becoming muffled by the avalanche of toxic content posted on social media platforms. This high level of toxic content prevents the dissemination of important and time-sensitive information and jeopardizes the sense of community that online social networks (OSNs) seek to cultivate. In this article, we present techniques to analyze toxic content and actors that propagated it on YouTube during the initial months after COVID-19 information was made public. Our dataset consists of 544 channels, 3,488 videos, 453,111 commenters, and 849,689 comments. We applied topic modeling based on Latent Dirichlet Allocation (LDA) to identify dominant topics and evolving trends within the comments on relevant videos. We conducted social network analysis (SNA) to detect influential commenters, and toxicity analysis to measure the health of the network. SNA allows us to identify the top toxic users in the network, which led to the creation of experiments simulating the impact of removal of these users on toxicity in the network. Through this work, we demonstrate not only how to identify toxic content related to COVID-19 on YouTube and the actors who propagated this toxicity, but also how social media companies and policy makers can use this work. This work is novel in that we devised a set of experiments in an attempt to show how if social media platforms eliminate certain toxic users, they can improve the overall health of the network by reducing the overall toxicity level. |
format | Online Article Text |
id | pubmed-9759669 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97596692022-12-19 Developing a socio-computational approach to examine toxicity propagation and regulation in COVID-19 discourse on YouTube Obadimu, Adewale Khaund, Tuja Mead, Esther Marcoux, Thomas Agarwal, Nitin Inf Process Manag Article As the novel coronavirus (COVID-19) continues to ravage the world at an unprecedented rate, formal recommendations from medical experts are becoming muffled by the avalanche of toxic content posted on social media platforms. This high level of toxic content prevents the dissemination of important and time-sensitive information and jeopardizes the sense of community that online social networks (OSNs) seek to cultivate. In this article, we present techniques to analyze toxic content and actors that propagated it on YouTube during the initial months after COVID-19 information was made public. Our dataset consists of 544 channels, 3,488 videos, 453,111 commenters, and 849,689 comments. We applied topic modeling based on Latent Dirichlet Allocation (LDA) to identify dominant topics and evolving trends within the comments on relevant videos. We conducted social network analysis (SNA) to detect influential commenters, and toxicity analysis to measure the health of the network. SNA allows us to identify the top toxic users in the network, which led to the creation of experiments simulating the impact of removal of these users on toxicity in the network. Through this work, we demonstrate not only how to identify toxic content related to COVID-19 on YouTube and the actors who propagated this toxicity, but also how social media companies and policy makers can use this work. This work is novel in that we devised a set of experiments in an attempt to show how if social media platforms eliminate certain toxic users, they can improve the overall health of the network by reducing the overall toxicity level. Elsevier Ltd. 2021-09 2021-06-10 /pmc/articles/PMC9759669/ /pubmed/36567973 http://dx.doi.org/10.1016/j.ipm.2021.102660 Text en © 2021 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 Obadimu, Adewale Khaund, Tuja Mead, Esther Marcoux, Thomas Agarwal, Nitin Developing a socio-computational approach to examine toxicity propagation and regulation in COVID-19 discourse on YouTube |
title | Developing a socio-computational approach to examine toxicity propagation and regulation in COVID-19 discourse on YouTube |
title_full | Developing a socio-computational approach to examine toxicity propagation and regulation in COVID-19 discourse on YouTube |
title_fullStr | Developing a socio-computational approach to examine toxicity propagation and regulation in COVID-19 discourse on YouTube |
title_full_unstemmed | Developing a socio-computational approach to examine toxicity propagation and regulation in COVID-19 discourse on YouTube |
title_short | Developing a socio-computational approach to examine toxicity propagation and regulation in COVID-19 discourse on YouTube |
title_sort | developing a socio-computational approach to examine toxicity propagation and regulation in covid-19 discourse on youtube |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9759669/ https://www.ncbi.nlm.nih.gov/pubmed/36567973 http://dx.doi.org/10.1016/j.ipm.2021.102660 |
work_keys_str_mv | AT obadimuadewale developingasociocomputationalapproachtoexaminetoxicitypropagationandregulationincovid19discourseonyoutube AT khaundtuja developingasociocomputationalapproachtoexaminetoxicitypropagationandregulationincovid19discourseonyoutube AT meadesther developingasociocomputationalapproachtoexaminetoxicitypropagationandregulationincovid19discourseonyoutube AT marcouxthomas developingasociocomputationalapproachtoexaminetoxicitypropagationandregulationincovid19discourseonyoutube AT agarwalnitin developingasociocomputationalapproachtoexaminetoxicitypropagationandregulationincovid19discourseonyoutube |