Session Introduction

This paper describes topics pertaining to the session, "Social Media Mining for Public Health Monitoring and Surveillance," at the Pacific Symposium on Biocomputing (PSB) 2016. In addition to summarizing the content of the session, this paper also surveys recent research on using social media data to study public health. The survey is organized into sections describing recent progress in public health problems, computational methods, and social implications.

[1]  Horace K. Gifpen.  The American Journal of Tropical Medicine and Hygiene , 1952 .

[2]  H Herken,et al.  Drug Safety , 1980, Medizinische Klinik.

[3]  Robert L. Berg,et al.  American Journal of Preventive Medicine , 1986, The American Journal of Medicine.

[4]  I. McCusker.  Health policy. , 2022, South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde.

[5]  L. Ohno-Machado.  Journal of Biomedical Informatics , 2001 .

[6]  J. Bromseth,et al.  Public places – public activities ? Methodological approaches and ethical dilemmas in research on computer-mediated communication contexts , 2002 .

[7]  Gunther Eysenbach,et al.  Infodemiology: Tracking Flu-Related Searches on the Web for Syndromic Surveillance , 2006, AMIA.

[8]  R. Rosenfeld.  Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[9]  Noémie Elhadad,et al.  Detecting salient aspects in online reviews of health providers. , 2010, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[10]  D. Mabey,et al.  Neglected tropical diseases. , 2010, British Medical Bulletin.

[11]  Aron Culotta,et al.  Towards detecting influenza epidemics by analyzing Twitter messages , 2010, SOMA '10.

[12]  Jian Yang,et al.  Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks , 2010, BioNLP@ACL.

[13]  Oren Etzioni,et al.  Named Entity Recognition in Tweets: An Experimental Study , 2011, EMNLP.

[14]  Christophe G. Giraud-Carrier,et al.  Identifying Health-Related Topics on Twitter - An Exploration of Tobacco-Related Tweets as a Test Topic , 2011, SBP.

[15]  Mizuki Morita,et al.  Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter , 2011, EMNLP.

[16]  Krishna P. Gummadi,et al.  Analyzing facebook privacy settings: user expectations vs. reality , 2011, IMC '11.

[17]  Brendan T. O'Connor,et al.  Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments , 2010, ACL.

[18]  Virgílio A. F. Almeida,et al.  Dengue surveillance based on a computational model of spatio-temporal locality of Twitter , 2011, WebSci '11.

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

[20]  Azadeh Nikfarjam,et al.  Pattern mining for extraction of mentions of Adverse Drug Reactions from user comments. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[21]  Owen Rambow,et al.  Sentiment Analysis of Twitter Data , 2011 .

[22]  Ernesto Diaz-Aviles,et al.  Tracking Twitter for epidemic intelligence: case study: EHEC/HUS outbreak in Germany, 2011 , 2012, WebSci '12.

[23]  Libby N Brockman,et al.  Associations between displayed alcohol references on Facebook and problem drinking among college students. , 2012, Archives of pediatrics & adolescent medicine.

[24]  Fan Yu,et al.  Towards large-scale twitter mining for drug-related adverse events , 2012, SHB '12.

[25]  Mark Dredze,et al.  Malpractice and Malcontent: Analyzing Medical Complaints in Twitter , 2012, AAAI Fall Symposium: Information Retrieval and Knowledge Discovery in Biomedical Text.

[26]  Barry Haddow,et al.  Proceedings of NAACL-HLT 2013 , 2013 .

[27]  Ophir Frieder,et al.  Extracting Adverse Drug Reactions from Forum Posts and Linking them to Drugs , 2013 .

[28]  Nazli Goharian,et al.  ADRTrace: Detecting Expected and Unexpected Adverse Drug Reactions from User Reviews on Social Media Sites , 2013, ECIR.

[29]  Marcel Salathé,et al.  Validating models for disease detection using twitter , 2013, WWW.

[30]  Jacob Eisenstein,et al.  What to do about bad language on the internet , 2013, NAACL.

[31]  A. V. Wersch,et al.  Cyberpsychology behavior and social networking , 2013 .

[32]  Nicholas Genes.  Twitter Discussions of Nonmedical Prescription Drug Use Correlate with Federal Survey Data , 2014 .

[33]  Maria Kvist,et al.  Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi) , 2014 .

[34]  Alexander R. Statnikov,et al.  Text classification for automatic detection of alcohol use-related tweets: A feasibility study , 2014, Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014).

[35]  Paloma Martínez,et al.  Detecting drugs and adverse events from Spanish social media streams , 2014, Louhi@EACL.

[36]  Tao Li,et al.  A Joint Local-Global Approach for Medical Terminology Assignment , 2014, MedIR@SIGIR.

[37]  Daniel Fried,et al.  Analyzing the language of food on social media , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[38]  Azadeh Nikfarjam,et al.  Mining Twitter for Adverse Drug Reaction Mentions : A Corpus and Classification Benchmark , 2014 .

[39]  Irene Pala,et al.  BMC Medical Informatics and Decision Making , 2014, BMC Medical Informatics and Decision Making.

[40]  D. Dobson,et al.  PLOS Neglected Tropical Diseases , 2014 .

[41]  Jeffrey Heer,et al.  Forum77: An Analysis of an Online Health Forum Dedicated to Addiction Recovery , 2015, CSCW.

[42]  Matthew Richardson,et al.  Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media , 2015, KDD.

[43]  Michelle Odlum.  How Twitter Can Support Early Warning Systems in Ebola Outbreak Surveillance , 2015 .

[44]  Mark Dredze,et al.  Worldwide Influenza Surveillance through Twitter , 2015, AAAI Workshop: WWW and Public Health Intelligence.

[45]  James M Wilson,et al.  The Next Chapter. , 2015, Human gene therapy.

[46]  Hamed Haddadi,et al.  #FoodPorn: Obesity Patterns in Culinary Interactions , 2015, Digital Health.

[47]  Aron Culotta,et al.  Using matched samples to estimate the effects of exercise on mental health from twitter , 2015, AAAI 2015.

[48]  Wayne G. Lutters,et al.  Ethics for Studying Online Sociotechnical Systems in a Big Data World , 2015, CSCW Companion.

[49]  Munmun De Choudhury,et al.  Measuring and Characterizing Nutritional Information of Food and Ingestion Content in Instagram , 2015, WWW.

[50]  Karin M. Verspoor,et al.  Towards Early Discovery of Salient Health Threats: A Social Media Emotion Classification Technique , 2016, PSB.

[51]  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.

[52]  Megha Agrawal,et al.  Predicting Individual Well-Being Through the Language of Social Media , 2016, PSB.

[53]  Palakorn Achananuparp,et al.  Insights from Machine-Learned Diet Success Prediction , 2015, PSB.

[54]  Lang Li,et al.  Monitoring Potential Drug Interactions and Reactions via Network Analysis of Instagram User Timelines , 2015, PSB.

[55]  L. Christophorou.  Science , 2018, Emerging Dynamics: Science, Energy, Society and Values.