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An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests

Trust in the government is an important dimension of happiness according to the World Happiness Report (Skelton, 2022). Recently, social media platforms have been exploited to erode this trust by spreading hate-filled, violent, anti-government sentiment. This trend was amplified during the COVID-19...

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Autores principales: Nguyen, Hieu, Gokhale, Swapna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748841/
https://www.ncbi.nlm.nih.gov/pubmed/36532815
http://dx.doi.org/10.7717/peerj-cs.1127
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author Nguyen, Hieu
Gokhale, Swapna
author_facet Nguyen, Hieu
Gokhale, Swapna
author_sort Nguyen, Hieu
collection PubMed
description Trust in the government is an important dimension of happiness according to the World Happiness Report (Skelton, 2022). Recently, social media platforms have been exploited to erode this trust by spreading hate-filled, violent, anti-government sentiment. This trend was amplified during the COVID-19 pandemic to protest the government-imposed, unpopular public health and safety measures to curb the spread of the coronavirus. Detection and demotion of anti-government rhetoric, especially during turbulent times such as the COVID-19 pandemic, can prevent the escalation of such sentiment into social unrest, physical violence, and turmoil. This article presents a classification framework to identify anti-government sentiment on Twitter during politically motivated, anti-lockdown protests that occurred in the capital of Michigan. From the tweets collected and labeled during the pair of protests, a rich set of features was computed from both structured and unstructured data. Employing feature engineering grounded in statistical, importance, and principal components analysis, subsets of these features are selected to train popular machine learning classifiers. The classifiers can efficiently detect tweets that promote an anti-government view with around 85% accuracy. With an F1-score of 0.82, the classifiers balance precision against recall, optimizing between false positives and false negatives. The classifiers thus demonstrate the feasibility of separating anti-government content from social media dialogue in a chaotic, emotionally charged real-life situation, and open opportunities for future research.
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spelling pubmed-97488412022-12-15 An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests Nguyen, Hieu Gokhale, Swapna PeerJ Comput Sci Computational Science Trust in the government is an important dimension of happiness according to the World Happiness Report (Skelton, 2022). Recently, social media platforms have been exploited to erode this trust by spreading hate-filled, violent, anti-government sentiment. This trend was amplified during the COVID-19 pandemic to protest the government-imposed, unpopular public health and safety measures to curb the spread of the coronavirus. Detection and demotion of anti-government rhetoric, especially during turbulent times such as the COVID-19 pandemic, can prevent the escalation of such sentiment into social unrest, physical violence, and turmoil. This article presents a classification framework to identify anti-government sentiment on Twitter during politically motivated, anti-lockdown protests that occurred in the capital of Michigan. From the tweets collected and labeled during the pair of protests, a rich set of features was computed from both structured and unstructured data. Employing feature engineering grounded in statistical, importance, and principal components analysis, subsets of these features are selected to train popular machine learning classifiers. The classifiers can efficiently detect tweets that promote an anti-government view with around 85% accuracy. With an F1-score of 0.82, the classifiers balance precision against recall, optimizing between false positives and false negatives. The classifiers thus demonstrate the feasibility of separating anti-government content from social media dialogue in a chaotic, emotionally charged real-life situation, and open opportunities for future research. PeerJ Inc. 2022-11-25 /pmc/articles/PMC9748841/ /pubmed/36532815 http://dx.doi.org/10.7717/peerj-cs.1127 Text en © 2022 Nguyen and Gokhale https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Computational Science
Nguyen, Hieu
Gokhale, Swapna
An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests
title An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests
title_full An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests
title_fullStr An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests
title_full_unstemmed An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests
title_short An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests
title_sort efficient approach to identifying anti-government sentiment on twitter during michigan protests
topic Computational Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748841/
https://www.ncbi.nlm.nih.gov/pubmed/36532815
http://dx.doi.org/10.7717/peerj-cs.1127
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