A scoping review of the use of Twitter for public health research
暂无分享,去创建一个
Beatriz de la Iglesia | Iain Lake | Obaghe Edeghere | Oduwa Edo-Osagie | I. Lake | B. D. L. Iglesia | O. Edeghere | Oduwa Edo-Osagie | B. Iglesia
[1] Yu-Chuan Li,et al. Utilizing different word representation methods for twitter data in adverse drug reactions extraction , 2015, 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI).
[2] Alok N. Choudhary,et al. Mining social media streams to improve public health allergy surveillance , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).
[3] Nadir Weibel,et al. Analyzing social media to characterize local HIV at-risk populations , 2015, Wireless Health.
[4] M. Shigematsu,et al. Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review , 2015, PloS one.
[5] Adrian B. R. Shatte,et al. Machine learning in mental health: a scoping review of methods and applications , 2019, Psychological Medicine.
[6] Olga Baysal,et al. Mining Twitter Data for Influenza Detection and Surveillance , 2016, 2016 IEEE/ACM International Workshop on Software Engineering in Healthcare Systems (SEHS).
[7] Marwan Bikdash,et al. Hybrid classification for tweets related to infection with influenza , 2015, SoutheastCon 2015.
[8] Ming-Hsiang Tsou,et al. Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza , 2016, PloS one.
[9] P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .
[10] K Denecke,et al. How to Exploit Twitter for Public Health Monitoring? , 2013, Methods of Information in Medicine.
[11] Thorsten Joachims,et al. Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.
[12] Alok N. Choudhary,et al. Forecasting Influenza Levels Using Real-Time Social Media Streams , 2017, 2017 IEEE International Conference on Healthcare Informatics (ICHI).
[13] Naren Ramakrishnan,et al. Syndromic surveillance of Flu on Twitter using weakly supervised temporal topic models , 2016, Data Mining and Knowledge Discovery.
[14] Aron Culotta,et al. Estimating county health statistics with twitter , 2014, CHI.
[15] John S. Brownstein,et al. Inferences about spatiotemporal variation in dengue virus transmission are sensitive to assumptions about human mobility: a case study using geolocated tweets from Lahore, Pakistan , 2018, EPJ Data Science.
[16] Hyekyung Woo,et al. Identification of Keywords From Twitter and Web Blog Posts to Detect Influenza Epidemics in Korea , 2017, Disaster Medicine and Public Health Preparedness.
[17] Karin M. Verspoor,et al. Towards Early Discovery of Salient Health Threats: A Social Media Emotion Classification Technique , 2016, PSB.
[18] C E Winslow,et al. THE UNTILLED FIELDS OF PUBLIC HEALTH. , 2017, Science.
[19] Evan Dennison Livelo,et al. Intelligent Dengue Infoveillance Using Gated Recurrent Neural Learning and Cross-Label Frequencies , 2018, 2018 IEEE International Conference on Agents (ICA).
[20] D. Moher,et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. , 2010, International journal of surgery.
[21] J. Brownstein,et al. Feasibility of using social media to monitor outdoor air pollution in London, England. , 2019, Preventive medicine.
[22] Marwan Bikdash,et al. Distance-based outliers method for detecting disease outbreaks using social media , 2016, SoutheastCon 2016.
[23] H. Arksey,et al. Scoping studies: towards a methodological framework , 2005 .
[24] Soon Ae Chun,et al. Enabling Real-Time Drug Abuse Detection in Tweets , 2017, 2017 IEEE 33rd International Conference on Data Engineering (ICDE).
[25] Lan Mu,et al. Effect of climate and seasonality on depressed mood among twitter users , 2015 .
[26] Hopin Lee,et al. Tweeting back: predicting new cases of back pain with mass social media data , 2016, J. Am. Medical Informatics Assoc..
[27] Elad Yom-Tov,et al. Detecting Disease Outbreaks in Mass Gatherings Using Internet Data Monitoring , 2015 .
[28] N. Heaivilin,et al. Public Health Surveillance of Dental Pain via Twitter , 2011, Journal of dental research.
[29] Hamman Samuel,et al. Context Prediction in the Social Web Using Applied Machine Learning: A Study of Canadian Tweeters , 2018, 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI).
[30] Gert R. G. Lanckriet,et al. Twitter-Based Detection of Illegal Online Sale of Prescription Opioid , 2017, American journal of public health.
[31] Taehyung Wang,et al. Social Network Data Mining Using Natural Language Processing and Density Based Clustering , 2014, 2014 IEEE International Conference on Semantic Computing.
[32] Timothy B. Patrick,et al. Social Media, Big Data, and Public Health Informatics: Ruminating Behavior of Depression Revealed through Twitter , 2015, 2015 48th Hawaii International Conference on System Sciences.
[33] Wenli Zhang,et al. Predicting Asthma-Related Emergency Department Visits Using Big Data , 2015, IEEE Journal of Biomedical and Health Informatics.
[34] Sophia Ananiadou,et al. Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts , 2016, J. Biomed. Informatics.
[35] Ernesto Diaz-Aviles,et al. Tracking Twitter for epidemic intelligence: case study: EHEC/HUS outbreak in Germany, 2011 , 2012, WebSci '12.
[36] Ophir Frieder,et al. Health-related hypothesis generation using social media data , 2015, Social Network Analysis and Mining.
[37] Robert L Cook,et al. Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis , 2016, JMIR public health and surveillance.
[38] Christophe Giraud-Carrier,et al. Epidemiology from Tweets: Estimating Misuse of Prescription Opioids in the USA from Social Media , 2017, Journal of Medical Toxicology.
[39] Malika Mahoui,et al. Social Media Sensing Framework for Population Health , 2019, 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC).
[40] Eiji Aramaki,et al. Conditional Density Estimation of Tweet Location: A Feature-Dependent Approach , 2017, MedInfo.
[41] Helmut Leopold,et al. Social Media , 2012, Elektrotech. Informationstechnik.
[42] T. Mackey,et al. Detection of illicit online sales of fentanyls via Twitter , 2017, F1000Research.
[43] Ingemar J. Cox,et al. On Infectious Intestinal Disease Surveillance using Social Media Content , 2016, Digital Health.
[44] Richard Pebody,et al. The added value of online user-generated content in traditional methods for influenza surveillance , 2018, Scientific Reports.
[45] Wagner Meira,et al. Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level , 2017, PLoS neglected tropical diseases.
[46] A. Rajić,et al. A scoping review of scoping reviews: advancing the approach and enhancing the consistency , 2014, Research synthesis methods.
[47] Alok N. Choudhary,et al. Real-time disease surveillance using Twitter data: demonstration on flu and cancer , 2013, KDD.
[48] Dilek Küçük,et al. Ontology-based automatic identification of public health-related Turkish tweets , 2017, Comput. Biol. Medicine.
[49] Samarth Swarup,et al. Semantic network analysis of vaccine sentiment in online social media. , 2017, Vaccine.
[50] Mei Han,et al. City-Wide Influenza Forecasting based on Multi-Source Data , 2018, 2018 IEEE International Conference on Big Data (Big Data).
[51] Marina Riga,et al. Investigating the Relationship between Social Media Content and Real-time Observations for Urban Air Quality and Public Health , 2014, WIMS '14.
[52] Marwan Bikdash,et al. From social media to public health surveillance: Word embedding based clustering method for twitter classification , 2017, SoutheastCon 2017.
[53] R. Weiss,et al. Using social media as a tool to predict syphilis. , 2017, Preventive medicine.
[54] S. Natarajan,et al. Public health allergy surveillance using micro-blogs , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).
[55] Sung Hoon Lim,et al. An unsupervised machine learning model for discovering latent infectious diseases using social media data , 2017, J. Biomed. Informatics.
[56] 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.
[57] Philip M. Massey,et al. Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter , 2016, Journal of medical Internet research.
[58] Jeremy Ginsberg,et al. Detecting influenza epidemics using search engine query data , 2009, Nature.
[59] Ireneus Kagashe,et al. Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data , 2017, Journal of medical Internet research.
[60] D. Buckeridge,et al. Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study , 2018, Canadian Journal of Public Health.
[61] Richard Bonneau,et al. Text Classification for Automatic Detection of E-Cigarette Use and Use for Smoking Cessation from Twitter: A Feasibility Pilot , 2016, PSB.
[62] Zina Ben Miled,et al. Digital Immunization Surveillance: Monitoring Flu Vaccination Rates Using Online Social Networks , 2017, 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS).
[63] Scott H. Burton,et al. Evaluating Social Media’s Capacity to Develop Engaged Audiences in Health Promotion Settings , 2013, Health Promotion Practice.
[64] Ümit V. Çatalyürek,et al. Syndromic Surveillance of Infectious Diseases meets Molecular Epidemiology in a Workflow and Phylogeographic Application , 2015, MedInfo.
[65] Chandler McClellan,et al. Using social media to monitor mental health discussions − evidence from Twitter , 2017, J. Am. Medical Informatics Assoc..
[66] G. Hejblum,et al. A systematic review of models for forecasting the number of emergency department visits , 2009, Emergency Medicine Journal.
[67] Triple S Project. Assessment of syndromic surveillance in Europe , 2011, The Lancet.
[68] Liang Zhao,et al. SimNest: Social Media Nested Epidemic Simulation via Online Semi-Supervised Deep Learning , 2015, 2015 IEEE International Conference on Data Mining.
[69] Michael J. Paul,et al. National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic , 2013, PloS one.
[70] T Sasikala,et al. Tracing out various diseases by analyzing Twitter data applying data mining techniques , 2017, 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS).
[71] Ophir Frieder,et al. A framework for detecting public health trends with Twitter , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).
[72] Naren Ramakrishnan,et al. Flu Gone Viral: Syndromic Surveillance of Flu on Twitter Using Temporal Topic Models , 2014, 2014 IEEE International Conference on Data Mining.
[73] Ahmed Abdeen Hamed,et al. T-Recs: Time-aware Twitter-based Drug Recommender System , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.
[74] Lan Mu,et al. GIS analysis of depression among Twitter users , 2015 .
[75] Sihem Amer-Yahia,et al. Health Monitoring on Social Media over Time , 2016, IEEE Transactions on Knowledge and Data Engineering.
[76] Henry A. Kautz,et al. Deploying nEmesis: Preventing Foodborne Illness by Data Mining Social Media , 2016, AI Mag..
[77] Sanmay Das,et al. Drugs or Dancing? Using Real-Time Machine Learning to Classify Streamed “Dabbing” Homograph Tweets , 2016, 2016 IEEE International Conference on Healthcare Informatics (ICHI).
[78] Chris Hankin,et al. DEFENDER: Detecting and Forecasting Epidemics Using Novel Data-Analytics for Enhanced Response , 2015, PloS one.
[79] Haiyan Wang,et al. Prediction of influenza-like illness based on the improved artificial tree algorithm and artificial neural network , 2018, Scientific Reports.
[80] Tsuyoshi Murata,et al. {m , 1934, ACML.
[81] Yukiko Kawai,et al. Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study , 2018, JMIR public health and surveillance.
[82] Fan Yu,et al. Towards large-scale twitter mining for drug-related adverse events , 2012, SHB '12.
[83] Michael D. Barnes,et al. Temporal variability of problem drinking on Twitter , 2012 .
[84] Muhammad Imran,et al. Classifying Information from Microblogs during Epidemics , 2017, DH.
[85] Meera Gandhi,et al. Earthquake Reporting System Development by Tweet Analysis with Approach Earthquake Alarm Systems , 2016 .
[86] Todd J. Bodnar,et al. Identifying Adverse Effects of HIV Drug Treatment and Associated Sentiments Using Twitter , 2015, JMIR public health and surveillance.
[87] Kevin A Padrez,et al. Twitter as a Tool for Health Research: A Systematic Review , 2017, American journal of public health.
[88] Yanfang Ye,et al. Adverse event detection by integrating twitter data and VAERS , 2018, Journal of Biomedical Semantics.
[89] K. Suzanne Barber,et al. Trust filter for disease surveillance: Identity , 2017, 2017 Intelligent Systems Conference (IntelliSys).
[90] Chris Hankin,et al. Real-time processing of social media with SENTINEL: A syndromic surveillance system incorporating deep learning for health classification , 2019, Inf. Process. Manag..
[91] Vasudeva Varma,et al. Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction mention extraction , 2017, BMC Bioinformatics.
[92] Patrick Breen,et al. Mining Pre-Exposure Prophylaxis Trends in Social Media , 2016, 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA).
[93] Michael J. Paul,et al. Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study , 2015, JMIR public health and surveillance.
[94] Virgílio A. F. Almeida,et al. Dengue surveillance based on a computational model of spatio-temporal locality of Twitter , 2011, WebSci '11.
[95] Suchendra M. Bhandarkar,et al. A Deep Learning Paradigm for Detection of Harmful Algal Blooms , 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).
[96] Jihoon Jung,et al. Social media responses to heat waves , 2017, International Journal of Biometeorology.
[97] Degui Zhi,et al. Tweeting about measles during stages of an outbreak: A semantic network approach to the framing of an emerging infectious disease , 2018, American Journal of Infection Control.
[98] Graciela Gonzalez-Hernandez,et al. Pharmacovigilance on Twitter? Mining Tweets for Adverse Drug Reactions , 2014, AMIA.
[99] A. Rasin,et al. Using Real-Time Social Media Technologies to Monitor Levels of Perceived Stress and Emotional State in College Students: A Web-Based Questionnaire Study , 2017, JMIR mental health.
[100] Haiyan Wang,et al. Regional Level Influenza Study with Geo-Tagged Twitter Data , 2016, Journal of Medical Systems.
[101] Ronaldo Menezes,et al. Mining location information from users' spatio-temporal data , 2017, 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).
[102] Hui Zhao,et al. Detecting Flu Transmission by Social Sensor in China , 2013, 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing.
[103] Yanfang Ye,et al. Semi-supervised Multi-instance Interpretable Models for Flu Shot Adverse Event Detection , 2018, 2018 IEEE International Conference on Big Data (Big Data).
[104] Benyuan Liu,et al. Predicting Flu Trends using Twitter data , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).
[105] Rok Sosic,et al. Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis , 2018, JMIR public health and surveillance.
[106] Melody Moh,et al. Efficient adverse drug event extraction using Twitter sentiment analysis , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).
[107] Bechara Choucair,et al. Health Department Use of Social Media to Identify Foodborne Illness — Chicago, Illinois, 2013–2014 , 2014, MMWR. Morbidity and mortality weekly report.