{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:20:12Z","timestamp":1773800412929,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"US Government, including the US Department of Education under the Title III Historically Black Graduate Institutions (HBGI) grant"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1145\/3459930.3469550","type":"proceedings-article","created":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T18:30:10Z","timestamp":1627669810000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Surveillance of COVID-19 pandemic using social media"],"prefix":"10.1145","author":[{"given":"Christopher","family":"Whitfield","sequence":"first","affiliation":[{"name":"North Carolina A&T University"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"North Carolina A&T University"}]},{"given":"Mohd","family":"Anwar","sequence":"additional","affiliation":[{"name":"North Carolina A&T University"}]}],"member":"320","published-online":{"date-parts":[[2021,8]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2016.07.030"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Jelodar H. Wang Y. Orji R. and Huang H. 2020. Deep sentiment classification and topic discovery on novel coronavirus or covid-19 online discussions: Nlp using lstm recurrent neural network approach. arXiv preprint arXiv:2004.11695.","DOI":"10.1101\/2020.04.22.054973"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Glowacki E.M. Lazard A.J. Wilcox G.B. Mackert M. and Bernhardt J.M. 2016. Identifying the public's concerns and the Centers for Disease Control and Prevention's reactions during a health crisis: An analysis of a Zika live Twitter chat. American journal of infection control 44 1709--1711.","DOI":"10.1016\/j.ajic.2016.05.025"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Lazard A.J. Scheinfeld E. Bernhardt J.M. Wilcox G.B. and Suran M. 2015. Detecting themes of public concern: a text mining analysis of the Centers for Disease Control and Prevention's Ebola live Twitter chat. American journal of infection control 43 1109--1111.","DOI":"10.1016\/j.ajic.2015.05.025"},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of the 53rd Hawaii International Conference on System Sciences.","author":"Boit J.","unstructured":"Boit, J. and El-Gayar, O. 2020. Topical mining of malaria using social media. A text mining approach. In Proceedings of the 53rd Hawaii International Conference on System Sciences."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3011123"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/jtm\/taaa031"},{"key":"e_1_3_2_1_8_1","unstructured":"Xue J. Chen J. Hu R. Chen C. Zheng C. Liu X. and Zhu T. Twitter discussions and emotions about COVID-19 pandemic: a machine learning approach."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.2196\/19016"},{"key":"e_1_3_2_1_10_1","unstructured":"Doogan C. Buntine W. Linger H. and Brunt S. Public Perceptions and Attitudes Toward COVID-19 Nonpharmaceutical Interventions Across Six Countries: A Topic Modeling Analysis of Twitter Data."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3396956.3397000"},{"key":"e_1_3_2_1_12_1","unstructured":"Fang Z. and Costas R. 2020. Tracking the Twitter attention around the research efforts on the COVID-19 pandemic. arXiv preprint arXiv:2006.05783."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2020.1369"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Yin H. Yang S. and Li J. 2020. Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media. arXiv preprint arXiv:2007.02304.","DOI":"10.1007\/978-3-030-65390-3_46"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.2196\/19273"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11606-020-05988-8"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.2196\/19509"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Zhao Y. and Xu H. 2020. Chinese public attention to COVID-19 epidemic: Based on social media. medRxiv.","DOI":"10.1101\/2020.03.18.20038026"},{"key":"e_1_3_2_1_19_1","volume-title":"Eighth international AAAI conference on weblogs and social media.","author":"De Choudhury M.","unstructured":"De Choudhury, M. and De, S. 2014. Mental health discourse on reddit: Self-disclosure, social support, and anonymity. In Eighth international AAAI conference on weblogs and social media."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Low D.M. Rumker L. Talkar T. Torous J. Cecchi G. and Ghosh S. 2020. Natural language processing reveals vulnerable mental health support groups and heightened health anxiety on Reddit during COVID-19.","DOI":"10.31234\/osf.io\/xvwcy"},{"key":"e_1_3_2_1_21_1","volume-title":"Annual Symposium proceedings. AMIA Symposium","author":"Park A.","year":"2017","unstructured":"Park, A. and Conway, M. 2018. Tracking Health Related Discussions on Reddit for Public Health Applications. AMIA ... Annual Symposium proceedings. AMIA Symposium 2017, 1362--1371."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Conway M. Hu M. and Chapman W.W. 2019. Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data. Yearbook of medical informatics 28 208--217.","DOI":"10.1055\/s-0039-1677918"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11606-020-05889-w"},{"key":"e_1_3_2_1_24_1","volume-title":"Annual Symposium proceedings. AMIA Symposium","author":"Pandrekar S.","year":"2018","unstructured":"Pandrekar, S., Chen, X., Gopalkrishna, G., Srivastava, A., Saltz, M., Saltz, J. and Wang, F. 2018. Social Media Based Analysis of Opioid Epidemic Using Reddit. AMIA ... Annual Symposium proceedings. AMIA Symposium 2018, 867--876."},{"key":"e_1_3_2_1_25_1","volume-title":"Proceedings of the Proceedings of the First Workshop on Social Media Analytics, Washington D.C., District of Columbia2010 Association for Computing Machinery, 80--88","author":"Hong L.","unstructured":"Hong, L. and Davison, B.D. 2010. Empirical study of topic modeling in Twitter. In Proceedings of the Proceedings of the First Workshop on Social Media Analytics, Washington D.C., District of Columbia2010 Association for Computing Machinery, 80--88."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Denecke K. Krieck M. Otrusina L. Smrz P. Dolog P. Nejdl W. and Velasco E. 2013. How to exploit twitter for public health monitoring? Methods of information in medicine 52 326--339.","DOI":"10.3414\/ME12-02-0010"},{"key":"e_1_3_2_1_27_1","volume-title":"Proceedings of the 19th ACM international conference on Information and knowledge management","author":"Fisichella M.","unstructured":"Fisichella, M., Stewart, A., Denecke, K. and Nejdl, W. 2010. Unsupervised public health event detection for epidemic intelligence. In Proceedings of the 19th ACM international conference on Information and knowledge management, 1881--1884."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W15-3821"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btn534"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-02175-6"},{"key":"e_1_3_2_1_31_1","volume-title":"Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 1532--1543","author":"Pennington J.","unstructured":"Pennington, J., Socher, R. and Manning, C.D. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 1532--1543."},{"key":"e_1_3_2_1_32_1","unstructured":"Srivastava N. Hinton G. Krizhevsky A. Sutskever I. and Salakhutdinov R. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15 1929--1958."},{"key":"e_1_3_2_1_33_1","volume-title":"Tenth international AAAI conference on web and social media.","author":"Alvarez-Melis D.","unstructured":"Alvarez-Melis, D. and Saveski, M. 2016. Topic modeling in twitter: Aggregating tweets by conversations. In Tenth international AAAI conference on web and social media."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-6894-4"},{"key":"e_1_3_2_1_35_1","volume-title":"-F","author":"Long Q.-X.","year":"2020","unstructured":"Long, Q.-X., Liu, B.-Z., Deng, H.-J., Wu, G.-C., Deng, K., Chen, Y.-K., Liao, P., Qiu, J.-F., Lin, Y. and Cai, X.-F. 2020. Antibody responses to SARS-CoV-2 in patients with COVID-19. Nature Medicine, 1--4."}],"event":{"name":"BCB '21: 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","location":"Gainesville Florida","acronym":"BCB '21","sponsor":["SIGBIOM ACM Special Interest Group on Biomedical Computing"]},"container-title":["Proceedings of the 12th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459930.3469550","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3459930.3469550","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:17:43Z","timestamp":1750191463000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459930.3469550"}},"subtitle":["a reddit study in North Carolina"],"short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":35,"alternative-id":["10.1145\/3459930.3469550","10.1145\/3459930"],"URL":"https:\/\/doi.org\/10.1145\/3459930.3469550","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]},"assertion":[{"value":"2021-08-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}