Analysing Relevant Diseases from Iberian Tweets

The Internet constitutes a huge source of information that can be exploited by individuals in many different ways. With the increasing use of social networks and blogs, the Internet is now used not only as an information source but also to disseminate personal health information. In this paper we exploit the wealth of user-generated data, available through the micro-blogging service Twitter, to estimate and track the incidence of health conditions in society, specifically in Portugal and Spain. We present results for the acquisition of relevant tweets for a set of four different conditions (flu, depression, pregnancy and eating disorders) and for the binary classification of these tweets as relevant or not for each case. The results obtained, ranging in AUC from 0.7 to 0.87, are very promising and indicate that such approach provides a feasible solution for measuring and tracking the evolution of many health related aspects within the society.

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

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

[3]  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.

[4]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

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

[6]  David A Asch,et al.  Decoding twitter: Surveillance and trends for cardiac arrest and resuscitation communication. , 2013, Resuscitation.

[7]  Nello Cristianini,et al.  Tracking the flu pandemic by monitoring the social web , 2010, 2010 2nd International Workshop on Cognitive Information Processing.

[8]  N. Heaivilin,et al.  Public Health Surveillance of Dental Pain via Twitter , 2011, Journal of dental research.

[9]  Aron Culotta,et al.  Detecting influenza outbreaks by analyzing Twitter messages , 2010, ArXiv.

[10]  J. Brownstein,et al.  Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. , 2012, The American journal of tropical medicine and hygiene.

[11]  E. Larson,et al.  Dissemination of health information through social networks: twitter and antibiotics. , 2010, American journal of infection control.

[12]  A Lyon,et al.  Comparison of web-based biosecurity intelligence systems: BioCaster, EpiSPIDER and HealthMap. , 2012, Transboundary and emerging diseases.

[13]  Sérgio Matos,et al.  Predicting Flu Incidence from Portuguese Tweets , 2013, IWBBIO.

[14]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

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