Mertens, 2020, Fear of the coronavirus (COVID-19): predictors in an online study conducted in March 2020, J. Anxiety Disord., 74, 10.1016/j.janxdis.2020.102258
Han, 2019, Using social media to mine and analyze public sentiment during a disaster: a case study of the 2018 Shouguang City Flood in China, ISPRS Int. J. Geo-Inf., 8, 185, 10.3390/ijgi8040185
Wang, 2018, Social media analytics for natural disaster management, Int. J. Geogr. Inf. Sci., 32, 49, 10.1080/13658816.2017.1367003
Chae, 2014, Public behavior response analysis in disaster events utilizing visual analytics of microblog data, Comput. Graph., 38, 51, 10.1016/j.cag.2013.10.008
Ginsberg, 2009, Detecting influenza epidemics using search engine query data, Nature, 457, 1012, 10.1038/nature07634
Nagar, 2014, A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives, J. Med. Internet Res., 16, e236, 10.2196/jmir.3416
Ye, 2016
Shin, 2016, High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea, Sci. Rep., 6, 1, 10.1038/srep32920
Lee, 2013, Real-Time disease surveillance using twitter data:Demonstration on flu and cancer, Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., F1288, 1474
Sinnenberg, 2017, Twitter as a tool for health research: a systematic review, Am. J. Publ. Health, 107, e1, 10.2105/AJPH.2016.303512
Gomez-Lopez, 2017, Using social media to identify sources of healthy food in urban neighborhoods, J. Urban Health, 94, 429, 10.1007/s11524-017-0154-1
Ghosh, 2013, What are we ‘tweeting’about obesity? Mapping tweets with topic modeling and Geographic Information System, Cartogr. Geogr. Inf. Sci., 40, 90, 10.1080/15230406.2013.776210
Chen, 2014, Does food environment influence food choices? A geographical analysis through ‘tweets, Appl. Geogr., 51, 82, 10.1016/j.apgeog.2014.04.003
Nguyen, 2016, Leveraging geotagged Twitter data to examine neighborhood happiness, diet, and physical activity, Appl. Geogr., 73, 77, 10.1016/j.apgeog.2016.06.003
Yang, 2015, GIS analysis of depression among Twitter users, Appl. Geogr., 60, 217, 10.1016/j.apgeog.2014.10.016
Eichstaedt, 2015, Psychological language on Twitter predicts county-level heart disease mortality, Psychol. Sci., 26, 159, 10.1177/0956797614557867
Gibbons, 2019, Twitter-based measures of neighborhood sentiment as predictors of residential population health, PLoS One, 14, 1, 10.1371/journal.pone.0219550
Tsao, 2021
Liu, 2021, Public attitudes toward COVID-19 vaccines on English-language Twitter: a sentiment analysis, Vaccine, 39, 5499, 10.1016/j.vaccine.2021.08.058
Han, 2020, Using social media to mine and analyze public opinion related to COVID-19 in China, Int. J. Environ. Res. Publ. Health, 17, 10.3390/ijerph17082788
Benis, 2021, Reasons for taking the COVID-19 vaccine by US social media users, Vaccines, 9, 315, 10.3390/vaccines9040315
Mir, 2021
Lyu, 2021, Covid-19 vaccine-related discussion on twitter: topic modeling and sentiment analysis, J. Med. Internet Res., 23, 10.2196/24435
Rosillo, 2021, Real time surveillance of COVID-19 space and time clusters during the summer 2020 in Spain, BMC Publ. Health, 21, 1, 10.1186/s12889-021-10961-z
Zhou, 2021, Monitoring global trends in Covid-19 vaccination intention and confidence: a social media-based deep learning study, medRxiv
Villavicencio, 2021, Twitter sentiment analysis towards covid-19 vaccines in the Philippines using naïve bayes, OR Inf., 12
No title.” [Online]. Available: https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/comirnaty-and-pfizer-biontech-covid-19-vaccine#:∼:text=On December 11%2C 2020,of age and older.
Heimerl, 2014, Word cloud explorer: text analytics based on word clouds, 1833
Nagel, 2013, The complex relationship of realspace events and messages in cyberspace: case study of influenza and pertussis using tweets, J. Med. Internet Res., 15, e2705, 10.2196/jmir.2705
Singh, 2018, Sentiment analysis using Machine Learning technique to predict outbreaks and epidemics, Int. J. Adv. Sci. Res., 3, 19
Chakraborty, 2020, Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—a study to show how popularity is affecting accuracy in social media, Appl. Soft Comput., 97, 10.1016/j.asoc.2020.106754
M. Abdulaziz, M. Alsolamy, A. Alotaibi, and A. Alabbas, “Topic Based Sentiment Analysis for COVID-19 Tweets.”.
No title.” [Online]. Available: http://sentiment.nrc.ca/lexicons-for-research/.
Gupta, 2017, Study of Twitter sentiment analysis using machine learning algorithms on Python, Int. J. Comput. Appl., 165, 29
No title.” [Online]. Available: https://www.geeksforgeeks.org/python-sentiment-analysis-using-vader/.
No title.” [Online]. Available: https://jackmckew.dev/sentiment-analysis-text-cleaning-in-python-with-vader.html.
Dahal, 2019, Topic modeling and sentiment analysis of global climate change tweets, Soc. Netw. Anal. Min., 9, 1, 10.1007/s13278-019-0568-8
Ye, 2016, Use of social media for the detection and analysis of infectious diseases in China, ISPRS Int. J. Geo-Inf., 5, 156, 10.3390/ijgi5090156
Blei, 2003, Latent dirichlet allocation, J. Mach. Learn. Res., 3, 993
Aslam, 2014, The reliability of tweets as a supplementary method of seasonal influenza surveillance, J. Med. Internet Res., 16, e250, 10.2196/jmir.3532
Su, 2021, Twitter-based analysis reveals differential COVID-19 concerns across areas with socioeconomic disparities, Comput. Biol. Med., 132, 10.1016/j.compbiomed.2021.104336
Okabe, 2009, A kernel density estimation method for networks, its computational method and a GIS‐based tool, Int. J. Geogr. Inf. Sci., 23, 7, 10.1080/13658810802475491
No title.” [Online]. Available: https://coronavirus.ohio.gov/wps/portal/gov/covid-19/resources/news-releases-news-you-can-use.
No title.” [Online]. Available: https://www.michigan.gov/coronavirus/0,9753,7-406-98158---Y,00.html.
No title.” [Online]. Available: https://www.ajmc.com/view/a-timeline-of-covid-19-vaccine-developments-in-2021.
Puri, 2020, Social media and vaccine hesitancy: new updates for the era of COVID-19 and globalized infectious diseases, Hum. Vaccines Immunother., 16, 2586, 10.1080/21645515.2020.1780846