Analysis of tweets on toothache during the COVID-19 pandemic using the CrystalFeel algorithm: a cross-sectional study
Abstract Background Reasons such as the lack of information on the COVID-19 disease, lack of proven treatment for it, uncertainty about the duration of the pandemic, or social isolation affect people’s mental health. This study aimed to analyze the emotional type and intensity in tweets on toothache...
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doaj-e1b0d987fc92472baec4702e84aeddfb2021-08-29T11:34:57ZengBMCBMC Oral Health1472-68312021-08-012111710.1186/s12903-021-01778-8Analysis of tweets on toothache during the COVID-19 pandemic using the CrystalFeel algorithm: a cross-sectional studyHalenur Altan0Alem Coşgun1Department of Pediatric Dentistry, Faculty of Dentistry, Tokat Gaziosmanpaşa UniversityDepartment of Pediatric Dentistry, Faculty of Dentistry, Tokat Gaziosmanpaşa UniversityAbstract Background Reasons such as the lack of information on the COVID-19 disease, lack of proven treatment for it, uncertainty about the duration of the pandemic, or social isolation affect people’s mental health. This study aimed to analyze the emotional type and intensity in tweets on toothache posted during the COVID-19 pandemic. Methods Using the Twitter Search API, we collected tweets in English associated with the keywords “Corona, Toothache” “Corona, Tooth, Pain” “Corona, Dental Pain” “Covid-19, Toothache” “Covid-19, Tooth, Pain” and “Covid-19, Dental Pain” posted between March 11, 2020 and June 30, 2020 all around the world. After the relevant inclusion and exclusion criteria were applied, 426 posts were selected and analyzed using the CrystalFeel algorithm, a sensitivity analytical technology with proven accuracy. The chi-square test (SPSS v23, IBM) was used to compare emotions and emotional intensities according to the words used. Results It was determined that 80.3% of the participants experienced fear and 61.7% had a negative emotional intensity. There was no statistically significant difference between the distributions of emotions according to the words without time distinction (p = 0.136). There was a statistically significant difference between the distributions of emotional intensity according to the words without time distinction (p = 0.006). The keyword “Corona, Toothache” was used the most frequently by 30.8% of the participants. Conclusions This study is the first to analyze the emotional reactions of individuals who experienced toothaches during the COVID-19 pandemic using the CrystalFeel algorithm. Monitoring the social media posts of individuals experiencing toothache during the pandemic will help reduce fear and anger emotions and design public information messages that are compatible with the target group’s needs.https://doi.org/10.1186/s12903-021-01778-8COVID-19PandemicEmotionsSocial mediaToothache |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Halenur Altan Alem Coşgun |
spellingShingle |
Halenur Altan Alem Coşgun Analysis of tweets on toothache during the COVID-19 pandemic using the CrystalFeel algorithm: a cross-sectional study BMC Oral Health COVID-19 Pandemic Emotions Social media Toothache |
author_facet |
Halenur Altan Alem Coşgun |
author_sort |
Halenur Altan |
title |
Analysis of tweets on toothache during the COVID-19 pandemic using the CrystalFeel algorithm: a cross-sectional study |
title_short |
Analysis of tweets on toothache during the COVID-19 pandemic using the CrystalFeel algorithm: a cross-sectional study |
title_full |
Analysis of tweets on toothache during the COVID-19 pandemic using the CrystalFeel algorithm: a cross-sectional study |
title_fullStr |
Analysis of tweets on toothache during the COVID-19 pandemic using the CrystalFeel algorithm: a cross-sectional study |
title_full_unstemmed |
Analysis of tweets on toothache during the COVID-19 pandemic using the CrystalFeel algorithm: a cross-sectional study |
title_sort |
analysis of tweets on toothache during the covid-19 pandemic using the crystalfeel algorithm: a cross-sectional study |
publisher |
BMC |
series |
BMC Oral Health |
issn |
1472-6831 |
publishDate |
2021-08-01 |
description |
Abstract Background Reasons such as the lack of information on the COVID-19 disease, lack of proven treatment for it, uncertainty about the duration of the pandemic, or social isolation affect people’s mental health. This study aimed to analyze the emotional type and intensity in tweets on toothache posted during the COVID-19 pandemic. Methods Using the Twitter Search API, we collected tweets in English associated with the keywords “Corona, Toothache” “Corona, Tooth, Pain” “Corona, Dental Pain” “Covid-19, Toothache” “Covid-19, Tooth, Pain” and “Covid-19, Dental Pain” posted between March 11, 2020 and June 30, 2020 all around the world. After the relevant inclusion and exclusion criteria were applied, 426 posts were selected and analyzed using the CrystalFeel algorithm, a sensitivity analytical technology with proven accuracy. The chi-square test (SPSS v23, IBM) was used to compare emotions and emotional intensities according to the words used. Results It was determined that 80.3% of the participants experienced fear and 61.7% had a negative emotional intensity. There was no statistically significant difference between the distributions of emotions according to the words without time distinction (p = 0.136). There was a statistically significant difference between the distributions of emotional intensity according to the words without time distinction (p = 0.006). The keyword “Corona, Toothache” was used the most frequently by 30.8% of the participants. Conclusions This study is the first to analyze the emotional reactions of individuals who experienced toothaches during the COVID-19 pandemic using the CrystalFeel algorithm. Monitoring the social media posts of individuals experiencing toothache during the pandemic will help reduce fear and anger emotions and design public information messages that are compatible with the target group’s needs. |
topic |
COVID-19 Pandemic Emotions Social media Toothache |
url |
https://doi.org/10.1186/s12903-021-01778-8 |
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