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The Perspectives of Individuals with Comorbidities Towards COVID-19 Booster Vaccine Shots in Twitter: A Social Media Analysis Using Natural Language Processing, Sentiment Analysis and Topic Modeling
Individuals with comorbidities (i.e., Diabetes Mellitus, hypertension, heart diseases) are more likely to develop a more severe form of coronavirus disease 2019 (COVID-19), thus, they should take necessary precautions to avoid infection with severe acute respiratory syndrome coronavirus–2 (SARS-CoV-...
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Published in: | Journal of pure & applied microbiology : an international research journal of microbiology 2023-03, Vol.17 (1), p.567-575 |
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creator | Praveen, S.V Sundar, R Vajrobol, Vajratiya Ittamalla, Rajesh Srividya, K Farahat, Ramadan Abdelmoez Chopra, Hitesh Rehman, Mohammad Ebad Ur Chakraborty, Chiranjib Dhama, Kuldeep |
description | Individuals with comorbidities (i.e., Diabetes Mellitus, hypertension, heart diseases) are more likely to develop a more severe form of coronavirus disease 2019 (COVID-19), thus, they should take necessary precautions to avoid infection with severe acute respiratory syndrome coronavirus–2 (SARS-CoV-2) and its emerging variants and subvariants by getting COVID-19 vaccination and booster doses. In this regard, we used text analytics techniques, specifically Natural Language Processing (NLP), to understand the perception of Twitter users having comorbidities (diabetes, hypertension, and heart diseases) towards the COVID-19 vaccine booster doses. Understanding and identifying Twitter users’ perceptions and perspectives will help the members of medical fraternities, governments, and policymakers to frame and implement a suitable public health policy for promoting the uptake of booster shots by such vulnerable people. A total of 176,540 tweets were identified through the scrapping process to understand the perception of individuals with the mentioned comorbidities regarding the COVID-19 booster dose. From sentiment analysis, it was revealed that 57.6% out of 176,540 tweets expressed negative sentiments about the COVID-19 vaccine booster doses. The reasons for negative expressions have been found using the topic modeling approach (i.e., risk factors, fear of myocardial fibrosis, stroke, or death, and using vaccines as bio-weapons). Of note, enhancing the COVID-19 vaccination drive by administering its booster doses to more and more people is of paramount importance for rendering higher protective immunity under the current threats of recently emerging newer Omicron subvariants which are presently causing a rise in cases in a few countries, such as China and others, and might lead to a feasible new wave of the pandemic with the surge in cases at the global level. |
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In this regard, we used text analytics techniques, specifically Natural Language Processing (NLP), to understand the perception of Twitter users having comorbidities (diabetes, hypertension, and heart diseases) towards the COVID-19 vaccine booster doses. Understanding and identifying Twitter users’ perceptions and perspectives will help the members of medical fraternities, governments, and policymakers to frame and implement a suitable public health policy for promoting the uptake of booster shots by such vulnerable people. A total of 176,540 tweets were identified through the scrapping process to understand the perception of individuals with the mentioned comorbidities regarding the COVID-19 booster dose. From sentiment analysis, it was revealed that 57.6% out of 176,540 tweets expressed negative sentiments about the COVID-19 vaccine booster doses. The reasons for negative expressions have been found using the topic modeling approach (i.e., risk factors, fear of myocardial fibrosis, stroke, or death, and using vaccines as bio-weapons). Of note, enhancing the COVID-19 vaccination drive by administering its booster doses to more and more people is of paramount importance for rendering higher protective immunity under the current threats of recently emerging newer Omicron subvariants which are presently causing a rise in cases in a few countries, such as China and others, and might lead to a feasible new wave of the pandemic with the surge in cases at the global level.</description><identifier>ISSN: 0973-7510</identifier><identifier>EISSN: 2581-690X</identifier><identifier>DOI: 10.22207/JPAM.17.1.54</identifier><language>eng</language><publisher>Oriental Scientific Publishing Company</publisher><subject>booster dose ; Comorbidity ; Computational linguistics ; Coronaviruses ; covid-19 ; Diabetes ; Disease susceptibility ; Epidemics ; Heart diseases ; Hypertension ; India ; Language processing ; Medical policy ; Natural language interfaces ; natural language processing ; Public health ; Risk factors ; Severe acute respiratory syndrome ; Social media ; text analytics ; Vaccination ; vaccine ; Vaccines</subject><ispartof>Journal of pure & applied microbiology : an international research journal of microbiology, 2023-03, Vol.17 (1), p.567-575</ispartof><rights>COPYRIGHT 2023 Oriental Scientific Publishing Company</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c365t-38d9ddb1ba6907631491ced72b200d0ac146de47a4025aee46c183a926e11fa23</cites><orcidid>0000-0001-7469-4752 ; 0000-0001-8118-4849 ; 0000-0003-0558-0916 ; 0000-0001-7452-2002 ; 0000-0002-6263-0812 ; 0000-0001-8867-7603 ; 0000-0002-1450-8839 ; 0000-0003-2892-0709 ; 0000-0002-3958-239X ; 0000-0002-2717-9253</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,2102,27924,27925</link.rule.ids></links><search><creatorcontrib>Praveen, S.V</creatorcontrib><creatorcontrib>Sundar, R</creatorcontrib><creatorcontrib>Vajrobol, Vajratiya</creatorcontrib><creatorcontrib>Ittamalla, Rajesh</creatorcontrib><creatorcontrib>Srividya, K</creatorcontrib><creatorcontrib>Farahat, Ramadan Abdelmoez</creatorcontrib><creatorcontrib>Chopra, Hitesh</creatorcontrib><creatorcontrib>Rehman, Mohammad Ebad Ur</creatorcontrib><creatorcontrib>Chakraborty, Chiranjib</creatorcontrib><creatorcontrib>Dhama, Kuldeep</creatorcontrib><title>The Perspectives of Individuals with Comorbidities Towards COVID-19 Booster Vaccine Shots in Twitter: A Social Media Analysis Using Natural Language Processing, Sentiment Analysis and Topic Modeling</title><title>Journal of pure & applied microbiology : an international research journal of microbiology</title><description>Individuals with comorbidities (i.e., Diabetes Mellitus, hypertension, heart diseases) are more likely to develop a more severe form of coronavirus disease 2019 (COVID-19), thus, they should take necessary precautions to avoid infection with severe acute respiratory syndrome coronavirus–2 (SARS-CoV-2) and its emerging variants and subvariants by getting COVID-19 vaccination and booster doses. In this regard, we used text analytics techniques, specifically Natural Language Processing (NLP), to understand the perception of Twitter users having comorbidities (diabetes, hypertension, and heart diseases) towards the COVID-19 vaccine booster doses. Understanding and identifying Twitter users’ perceptions and perspectives will help the members of medical fraternities, governments, and policymakers to frame and implement a suitable public health policy for promoting the uptake of booster shots by such vulnerable people. A total of 176,540 tweets were identified through the scrapping process to understand the perception of individuals with the mentioned comorbidities regarding the COVID-19 booster dose. From sentiment analysis, it was revealed that 57.6% out of 176,540 tweets expressed negative sentiments about the COVID-19 vaccine booster doses. The reasons for negative expressions have been found using the topic modeling approach (i.e., risk factors, fear of myocardial fibrosis, stroke, or death, and using vaccines as bio-weapons). Of note, enhancing the COVID-19 vaccination drive by administering its booster doses to more and more people is of paramount importance for rendering higher protective immunity under the current threats of recently emerging newer Omicron subvariants which are presently causing a rise in cases in a few countries, such as China and others, and might lead to a feasible new wave of the pandemic with the surge in cases at the global level.</description><subject>booster dose</subject><subject>Comorbidity</subject><subject>Computational linguistics</subject><subject>Coronaviruses</subject><subject>covid-19</subject><subject>Diabetes</subject><subject>Disease susceptibility</subject><subject>Epidemics</subject><subject>Heart diseases</subject><subject>Hypertension</subject><subject>India</subject><subject>Language processing</subject><subject>Medical policy</subject><subject>Natural language interfaces</subject><subject>natural language processing</subject><subject>Public health</subject><subject>Risk factors</subject><subject>Severe acute respiratory syndrome</subject><subject>Social media</subject><subject>text analytics</subject><subject>Vaccination</subject><subject>vaccine</subject><subject>Vaccines</subject><issn>0973-7510</issn><issn>2581-690X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNptkktrGzEQx5fSQk2SY--CXruuRvvubeu-XOwmYCf0JsaSdj1hVzLSOiFfsJ8rSlxaCkUgwfwf_BCTJG-Az4UQvHr__apdz6Gaw7zIXyQzUdSQlg3_-TKZ8abK0qoA_jq5COGWcw6lqOusniW_tnvDrowPB6MmujOBuY4traY70kccArunac8WbnR-R5omio6tu0evA1tc3iw_pdCwj86FyXh2g0qRNWyzd1NgZNk2pqPwgbVs4xThwNZGE7LW4vAQKLDrQLZnP3A6-iiu0PZH7COQd8qEJ-0d2xg70Rivvym0OkIcSLG102aItvPkVRdpzcXv9yy5_vJ5u_iWri6_LhftKlVZWUxpVutG6x3sMP5MVWaQN6CMrsROcK45KshLbfIKcy4KNCYvFdQZNqI0AB2K7CxZnnq1w1t58DSif5AOST4PnO8l-onUYGTXlFAVhWpiby5A1RwKJfIOUYDo6i52vT119RjtZDs3eVQjBSXbKhdFJOZldM3_44pHm5GUs6ajOP8nkJ4CyrsQvOn-YAKXz6sin1ZFQiVBFnn2CPBBshA</recordid><startdate>20230301</startdate><enddate>20230301</enddate><creator>Praveen, S.V</creator><creator>Sundar, R</creator><creator>Vajrobol, Vajratiya</creator><creator>Ittamalla, Rajesh</creator><creator>Srividya, K</creator><creator>Farahat, Ramadan Abdelmoez</creator><creator>Chopra, Hitesh</creator><creator>Rehman, Mohammad Ebad Ur</creator><creator>Chakraborty, Chiranjib</creator><creator>Dhama, Kuldeep</creator><general>Oriental Scientific Publishing Company</general><general>Journal of Pure and Applied Microbiology</general><scope>AAYXX</scope><scope>CITATION</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-7469-4752</orcidid><orcidid>https://orcid.org/0000-0001-8118-4849</orcidid><orcidid>https://orcid.org/0000-0003-0558-0916</orcidid><orcidid>https://orcid.org/0000-0001-7452-2002</orcidid><orcidid>https://orcid.org/0000-0002-6263-0812</orcidid><orcidid>https://orcid.org/0000-0001-8867-7603</orcidid><orcidid>https://orcid.org/0000-0002-1450-8839</orcidid><orcidid>https://orcid.org/0000-0003-2892-0709</orcidid><orcidid>https://orcid.org/0000-0002-3958-239X</orcidid><orcidid>https://orcid.org/0000-0002-2717-9253</orcidid></search><sort><creationdate>20230301</creationdate><title>The Perspectives of Individuals with Comorbidities Towards COVID-19 Booster Vaccine Shots in Twitter: A Social Media Analysis Using Natural Language Processing, Sentiment Analysis and Topic Modeling</title><author>Praveen, S.V ; 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In this regard, we used text analytics techniques, specifically Natural Language Processing (NLP), to understand the perception of Twitter users having comorbidities (diabetes, hypertension, and heart diseases) towards the COVID-19 vaccine booster doses. Understanding and identifying Twitter users’ perceptions and perspectives will help the members of medical fraternities, governments, and policymakers to frame and implement a suitable public health policy for promoting the uptake of booster shots by such vulnerable people. A total of 176,540 tweets were identified through the scrapping process to understand the perception of individuals with the mentioned comorbidities regarding the COVID-19 booster dose. From sentiment analysis, it was revealed that 57.6% out of 176,540 tweets expressed negative sentiments about the COVID-19 vaccine booster doses. The reasons for negative expressions have been found using the topic modeling approach (i.e., risk factors, fear of myocardial fibrosis, stroke, or death, and using vaccines as bio-weapons). Of note, enhancing the COVID-19 vaccination drive by administering its booster doses to more and more people is of paramount importance for rendering higher protective immunity under the current threats of recently emerging newer Omicron subvariants which are presently causing a rise in cases in a few countries, such as China and others, and might lead to a feasible new wave of the pandemic with the surge in cases at the global level.</abstract><pub>Oriental Scientific Publishing Company</pub><doi>10.22207/JPAM.17.1.54</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-7469-4752</orcidid><orcidid>https://orcid.org/0000-0001-8118-4849</orcidid><orcidid>https://orcid.org/0000-0003-0558-0916</orcidid><orcidid>https://orcid.org/0000-0001-7452-2002</orcidid><orcidid>https://orcid.org/0000-0002-6263-0812</orcidid><orcidid>https://orcid.org/0000-0001-8867-7603</orcidid><orcidid>https://orcid.org/0000-0002-1450-8839</orcidid><orcidid>https://orcid.org/0000-0003-2892-0709</orcidid><orcidid>https://orcid.org/0000-0002-3958-239X</orcidid><orcidid>https://orcid.org/0000-0002-2717-9253</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | booster dose Comorbidity Computational linguistics Coronaviruses covid-19 Diabetes Disease susceptibility Epidemics Heart diseases Hypertension India Language processing Medical policy Natural language interfaces natural language processing Public health Risk factors Severe acute respiratory syndrome Social media text analytics Vaccination vaccine Vaccines |
title | The Perspectives of Individuals with Comorbidities Towards COVID-19 Booster Vaccine Shots in Twitter: A Social Media Analysis Using Natural Language Processing, Sentiment Analysis and Topic Modeling |
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