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Predicting the popularity of tweets by analyzing public opinion and emotions in different stages of Covid-19 pandemic
In this study, public opinion and emotions regarding different stages of the Covid-19 pandemic from the outbreak of the disease to the distribution of vaccines were analyzed to predict the popularity of tweets. More than 1.25 million English tweets were collected, posted from January 20, 2020, to Ma...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author. Published by Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8677469/ http://dx.doi.org/10.1016/j.jjimei.2021.100053 |
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author | Mahdikhani, Maryam |
author_facet | Mahdikhani, Maryam |
author_sort | Mahdikhani, Maryam |
collection | PubMed |
description | In this study, public opinion and emotions regarding different stages of the Covid-19 pandemic from the outbreak of the disease to the distribution of vaccines were analyzed to predict the popularity of tweets. More than 1.25 million English tweets were collected, posted from January 20, 2020, to May 29, 2021. Five sets of content features, including topic analysis, topics plus TF-IDF vectorizer, bag of words (BOW) by TF-IDF vectorizer, document embedding, and document embedding plus TF-IDF vectorizer, were extracted and applied to supervised machine learning algorithms to generate a predictive model for the retweetability of posted tweets. The analysis showed that tweets with higher emotional intensity are more popular than tweets containing information on Covid-19 pandemic. This study can help to detect the public emotions during the pandemic and after vaccination and predict the retweetability of posted tweets in different stages of Covid-19 pandemic. |
format | Online Article Text |
id | pubmed-8677469 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Author. Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86774692021-12-17 Predicting the popularity of tweets by analyzing public opinion and emotions in different stages of Covid-19 pandemic Mahdikhani, Maryam International Journal of Information Management Data Insights Article In this study, public opinion and emotions regarding different stages of the Covid-19 pandemic from the outbreak of the disease to the distribution of vaccines were analyzed to predict the popularity of tweets. More than 1.25 million English tweets were collected, posted from January 20, 2020, to May 29, 2021. Five sets of content features, including topic analysis, topics plus TF-IDF vectorizer, bag of words (BOW) by TF-IDF vectorizer, document embedding, and document embedding plus TF-IDF vectorizer, were extracted and applied to supervised machine learning algorithms to generate a predictive model for the retweetability of posted tweets. The analysis showed that tweets with higher emotional intensity are more popular than tweets containing information on Covid-19 pandemic. This study can help to detect the public emotions during the pandemic and after vaccination and predict the retweetability of posted tweets in different stages of Covid-19 pandemic. The Author. Published by Elsevier Ltd. 2022-04 2021-12-17 /pmc/articles/PMC8677469/ http://dx.doi.org/10.1016/j.jjimei.2021.100053 Text en © 2021 The Author 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 Mahdikhani, Maryam Predicting the popularity of tweets by analyzing public opinion and emotions in different stages of Covid-19 pandemic |
title | Predicting the popularity of tweets by analyzing public opinion and emotions in different stages of Covid-19 pandemic |
title_full | Predicting the popularity of tweets by analyzing public opinion and emotions in different stages of Covid-19 pandemic |
title_fullStr | Predicting the popularity of tweets by analyzing public opinion and emotions in different stages of Covid-19 pandemic |
title_full_unstemmed | Predicting the popularity of tweets by analyzing public opinion and emotions in different stages of Covid-19 pandemic |
title_short | Predicting the popularity of tweets by analyzing public opinion and emotions in different stages of Covid-19 pandemic |
title_sort | predicting the popularity of tweets by analyzing public opinion and emotions in different stages of covid-19 pandemic |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8677469/ http://dx.doi.org/10.1016/j.jjimei.2021.100053 |
work_keys_str_mv | AT mahdikhanimaryam predictingthepopularityoftweetsbyanalyzingpublicopinionandemotionsindifferentstagesofcovid19pandemic |