COVID-19 Pandemic: Identifying Key Issues Using Social Media and Natural Language Processing

[1]  Rita Orji,et al.  Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach , 2020, IEEE Journal of Biomedical and Health Informatics.

[2]  Laura E. Barnes,et al.  "Is This an STD? Please Help!": Online Information Seeking for Sexually Transmitted Diseases on Reddit , 2018, ICWSM.

[3]  Xiaojun Wang,et al.  Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media , 2019, Comput. Hum. Behav..

[4]  N. Nasir,et al.  MISINFORMATION RELATED TO COVID-19 IN INDONESIA , 2020, Jurnal Administrasi Kesehatan Indonesia.

[5]  K. Steadman,et al.  When fear and misinformation go viral: Pharmacists' role in deterring medication misinformation during the 'infodemic' surrounding COVID-19 , 2020, Research in Social and Administrative Pharmacy.

[6]  Erik F. Tjong Kim Sang,et al.  Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition , 2003, CoNLL.

[7]  G. Iacobucci,et al.  Covid-19: Lack of PPE in care homes is risking spread of virus, leaders warn , 2020, BMJ.

[8]  W. Van Lancker,et al.  COVID-19, school closures, and child poverty: a social crisis in the making , 2020, The Lancet Public Health.

[9]  Saeed Hassanpour,et al.  Identifying substance use risk based on deep neural networks and Instagram social media data , 2018, Neuropsychopharmacology.

[10]  E. Goddard.  The impact of COVID‐19 on food retail and food service in Canada: Preliminary assessment , 2020, Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie.

[11]  Beatrice Santorini,et al.  The Penn Treebank: An Overview , 2003 .

[12]  Mishal Dholakia,et al.  The Impact of COVID-19 on Flight Networks , 2020, 2020 IEEE International Conference on Big Data (Big Data).

[13]  Martin McKee,et al.  The public health effect of economic crises and alternative policy responses in Europe: an empirical analysis , 2009, The Lancet.

[14]  P. Horby,et al.  Estimated global mortality associated with the first 12 months of 2009 pandemic influenza A H1N1 virus circulation: a modelling study. , 2012, The Lancet. Infectious diseases.

[15]  Heiko Spallek,et al.  Using Natural Language Processing to Enable In-depth Analysis of Clinical Messages Posted to an Internet Mailing List: A Feasibility Study , 2011, Journal of medical Internet research.

[16]  Nicholas Bloom,et al.  The Unprecedented Stock Market Impact of Covid-19 , 2020 .

[17]  Sanjoy Kumar Paul,et al.  A production recovery plan in manufacturing supply chains for a high-demand item during COVID-19 , 2020, International Journal of Physical Distribution & Logistics Management.

[18]  L. Pan,et al.  Impact of COVID‐19 on blood centres in Zhejiang province China , 2020, Vox sanguinis.

[19]  Shujhat Khan,et al.  Coronavirus: the spread of misinformation , 2020, BMC Medicine.

[20]  K. Timmis,et al.  The COVID‐19 pandemic: some lessons learned about crisis preparedness and management, and the need for international benchmarking to reduce deficits , 2020, Environmental microbiology.

[21]  Eoin Whelan,et al.  Why do People Share Misinformation during the COVID-19 Pandemic? , 2020, ArXiv.

[22]  Olivier Coibion,et al.  The Cost of the Covid-19 Crisis: Lockdowns, Macroeconomic Expectations, and Consumer Spending , 2020 .

[23]  M. Agha,et al.  The socio-economic implications of the coronavirus pandemic (COVID-19): A review , 2020, International Journal of Surgery.

[24]  Andrew R. McNeill,et al.  influence on UK vaccination and antiviral uptake during the 2009 H 1 N 1 pandemic , 2019 .

[25]  Alberto Maria Segre,et al.  The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic , 2011, PloS one.

[26]  G. Eysenbach,et al.  Social Media: A Review and Tutorial of Applications in Medicine and Health Care , 2014, Journal of medical Internet research.

[27]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[28]  Kate E. Jones,et al.  Global trends in emerging infectious diseases , 2008, Nature.

[29]  A. Siddiqui,et al.  H1N1 Influenza (Swine Flu) , 2018 .

[30]  Hegler Tissot,et al.  Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial , 2019, IEEE Journal of Biomedical and Health Informatics.

[31]  Q. Nguyen,et al.  Census Tract Food Tweets and Chronic Disease Outcomes in the U.S., 2015–2018 , 2019, International journal of environmental research and public health.

[32]  A. Fauci,et al.  The challenge of emerging and re-emerging infectious diseases , 2004, Nature.

[33]  Marsha Rosengarten,et al.  Making evidence and policy in public health emergencies: lessons from COVID-19 for adaptive evidence-making and intervention , 2020, Evidence & Policy: A Journal of Research, Debate and Practice.

[34]  H. Natarajan,et al.  Social Protection and Jobs Responses to COVID-19: A Real-Time Review of Country Measures , 2020 .

[35]  R. Dobson,et al.  Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial , 2020, IEEE Journal of Biomedical and Health Informatics.

[36]  Political Beliefs affect Compliance with Government Mandates , 2021 .

[37]  Andrew R. McNeill,et al.  Twitter Influence on UK Vaccination and Antiviral Uptake during the 2009 H1N1 Pandemic , 2016, Front. Public Health.

[38]  Helge B. D. Sørensen,et al.  Visualizing patient journals by combining vital signs monitoring and natural language processing , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[39]  David E Bloom,et al.  Emerging infectious diseases: A proactive approach , 2017, Proceedings of the National Academy of Sciences.

[40]  V. Earnshaw,et al.  Educate, Amplify, and Focus to Address COVID-19 Misinformation. , 2020, JAMA health forum.

[41]  Wolfram Kawohl,et al.  COVID-19, unemployment, and suicide , 2020, The Lancet Psychiatry.

[42]  Dongbo Wang,et al.  The influence of word normalization in English document clustering , 2012, 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE).

[43]  Rita Orji,et al.  Social Media and Sentiment Analysis: The Nigeria Presidential Election 2019 , 2019, 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON).

[44]  Megan L Ranney,et al.  Critical Supply Shortages - The Need for Ventilators and Personal Protective Equipment during the Covid-19 Pandemic. , 2020, The New England journal of medicine.

[45]  G. Gereffi.  What does the COVID-19 pandemic teach us about global value chains? The case of medical supplies , 2020, Journal of International Business Policy.

[46]  D. Jamison,et al.  The Inclusive Cost of Pandemic Influenza Risk , 2016 .

[47]  G. Eysenbach,et al.  Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak , 2010, PloS one.

[48]  Peter John.  The Digital Classroom: Harnessing Technology for the Future of Learning and Teaching , 2015 .

[49]  Rohini K. Srihari,et al.  Using Verbs and Adjectives to Automatically Classify Blog Sentiment , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[50]  Widodo Budiharto,et al.  Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis , 2018, Journal of Big Data.

[51]  Rita Orji,et al.  Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach , 2020, bioRxiv.

[52]  C. Chambers,et al.  Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach , 2021, JMIR medical informatics.

[53]  Lyle H. Ungar,et al.  Understanding and Measuring Psychological Stress using Social Media , 2018, ICWSM.

[54]  E. Holmes,et al.  A new coronavirus associated with human respiratory disease in China , 2020, Nature.

[55]  Mike Conway,et al.  Tracking Health Related Discussions on Reddit for Public Health Applications , 2017, AMIA.

[56]  P. Resnik.  Treebanks : Building and Using Parsed Corpora , 2022 .

[57]  Beatrice Santorini,et al.  Part-of-Speech Tagging Guidelines for the Penn Treebank Project (3rd Revision) , 1990 .

[58]  S. Kinner,et al.  Prisons and custodial settings are part of a comprehensive response to COVID-19 , 2020, The Lancet Public Health.

[59]  Oladapo Oyebode,et al.  Using Machine Learning and Thematic Analysis Methods to Evaluate Mental Health Apps Based on User Reviews , 2020, IEEE Access.

[60]  Johan Bos,et al.  Predicting the 2011 Dutch Senate Election Results with Twitter , 2012 .

[61]  How Are Small Businesses Adjusting to COVID-19? Early Evidence from a Survey , 2020 .

[62]  M. Mckee,et al.  Global burden of HIV, viral hepatitis, and tuberculosis in prisoners and detainees , 2016, The Lancet.

[63]  Jennifer A. Asmuth,et al.  Context Sensitivity of Relational Nouns , 2005 .

[64]  Matthew Motta,et al.  How Right-Leaning Media Coverage of COVID-19 Facilitated the Spread of Misinformation in the Early Stages of the Pandemic in the U.S. , 2020, Canadian Journal of Political Science.

[65]  Sergio L. Schmukler,et al.  Financing firms in hibernation during the COVID-19 pandemic , 2022, Journal of Financial Stability.

[66]  Eric Gilbert,et al.  VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text , 2014, ICWSM.

[67]  M. McHugh.  Interrater reliability: the kappa statistic , 2012, Biochemia medica.

[68]  N. Fullman,et al.  Pandemic Politics: Timing State-Level Social Distancing Responses to COVID-19 , 2020, medRxiv.

[69]  Beatrice Santorini.  Part-of-speech tagging guidelines for the penn treebank project , 1990 .

[70]  Daniel Dajun Zeng,et al.  Electronic cigarette usage patterns: a case study combining survey and social media data , 2018, J. Am. Medical Informatics Assoc..

[71]  Y. K. Tse,et al.  Examining customer perception and behaviour through social media research – An empirical study of the United Airlines overbooking crisis , 2019, Transportation Research Part E: Logistics and Transportation Review.

[72]  Rita Orji,et al.  Health, Psychosocial, and Social issues emanating from COVID-19 pandemic based on Social Media Comments using Natural Language Processing , 2020, ArXiv.

[73]  Daniel P Nogee,et al.  Covid-19 and the N95 respirator shortage: Closing the gap , 2020, Infection Control & Hospital Epidemiology.

[74]  E. Kostelich,et al.  To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic , 2020, Infectious Disease Modelling.

[75]  Ryan D. Duffy,et al.  Unemployment in the time of COVID-19: A research agenda☆ , 2020, Journal of Vocational Behavior.

[76]  Huan Liu,et al.  Data Mining in Social Media , 2011, Social Network Data Analytics.

[77]  Rita Orji,et al.  Detecting Factors Responsible for Diabetes Prevalence in Nigeria using Social Media and Machine Learning , 2019, 2019 15th International Conference on Network and Service Management (CNSM).

[78]  Mio Dosen,et al.  COVID-19 Information Center , 2021 .

[79]  Erik F. Tjong Kim Sang,et al.  Introduction to the CoNLL-2003 shared task , 2003 .

[80]  Mark Dredze,et al.  You Are What You Tweet: Analyzing Twitter for Public Health , 2011, ICWSM.

[81]  Risikat Oladoyin S. Dauda,et al.  Impact of COVID-19 Pandemic on Financial Health and Food Security: a Survey-Based Analysis , 2020 .

[82]  Ruifu Yang,et al.  An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China , 2020, Science.

[83]  Ankur Agarwal,et al.  A Natural Language Processing Framework for Assessing Hospital Readmissions for Patients With COPD , 2018, IEEE Journal of Biomedical and Health Informatics.

[84]  Matteo Cella,et al.  Measuring attitudes towards mental health using social media: investigating stigma and trivialisation , 2018, Social Psychiatry and Psychiatric Epidemiology.

[85]  Soojong Kim,et al.  Political partisanship influences behavioral responses to governors’ recommendations for COVID-19 prevention in the United States , 2020, Proceedings of the National Academy of Sciences.

[86]  Vasudeva Varma,et al.  Pattern based keyword extraction for contextual advertising , 2010, CIKM '10.

[87]  A. Gori,et al.  The COVID-19 pandemic preparedness ... or lack thereof: from China to Italy. , 2020, Global health & medicine.

[88]  Bing Liu,et al.  Sentence Subjectivity and Sentiment Classification , 2015, Sentiment Analysis.

[89]  R. Fairlie,et al.  The Impacts of COVID-19 on Minority Unemployment: First Evidence from April 2020 CPS Microdata , 2020, SSRN Electronic Journal.