title: Discovering health topics in social media using topic models.
abstract: By aggregating self-reported health statuses across millions of users, we seek to characterize the variety of health information discussed in Twitter. We describe a topic modeling framework for discovering health topics in Twitter, a social media website. This is an exploratory approach with the goal of understanding what health topics are commonly discussed in social media. This paper describes in detail a statistical topic model created for this purpose, the Ailment Topic Aspect Model (ATAM), as well as our system for filtering general Twitter data based on health keywords and supervised classification. We show how ATAM and other topic models can automatically infer health topics in 144 million Twitter messages from 2011 to 2013. ATAM discovered 13 coherent clusters of Twitter messages, some of which correlate with seasonal influenza (r = 0.689) and allergies (r = 0.810) temporal surveillance data, as well as exercise (r =  .534) and obesity (r =  -.631) related geographic survey data in the United States. These results demonstrate that it is possible to automatically discover topics that attain statistically significant correlations with ground truth data, despite using minimal human supervision and no historical data to train the model, in contrast to prior work. Additionally, these results demonstrate that a single general-purpose model can identify many different health topics in social media.
authors.affiliation: Department of Computer Science and Center for Language and Speech Processing, Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Paul
authors.affiliation: Department of Computer Science and Center for Language and Speech Processing, Johns Hopkins University, Baltimore, Maryland, United States of America; Human Language Technology Center of Excellence and Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Mark
authors.initials: M
authors.lastname: Dredze
mesh_terms: D000465:Algorithms; D057225:Data Mining; D006262:Health; D006801:Humans; D008962:Models, Theoretical; D061108:Social Media
references.citation: JAMA. 2014 Apr 9;311(14):1399-400
references.pmid: 24577162
references.citation: Am J Prev Med. 2013 Jul;45(1):122-129
references.pmid: 23790998
references.citation: Science. 2011 Sep 30;333(6051):1878-81
references.pmid: 21960633
references.citation: J Med Internet Res. 2011 Dec 19;13(4):e119
references.pmid: 22182518
references.citation: IEEE Trans Pattern Anal Mach Intell. 1984 Jun;6(6):721-41
references.pmid: 22499653
references.citation: Am J Infect Control. 2010 Apr;38(3):182-8
references.pmid: 20347636
references.citation: AMIA Annu Symp Proc. 2008 Nov 06;:485-9
references.pmid: 18999010
references.citation: J Dent Res. 2011 Sep;90(9):1047-51
references.pmid: 21768306
references.citation: Am J Trop Med Hyg. 2012 Jan;86(1):39-45
references.pmid: 22232449
references.citation: Proc Natl Acad Sci U S A. 2004 Apr 6;101 Suppl 1:5228-35
references.pmid: 14872004
references.citation: Arch Pediatr Adolesc Med. 2012 Feb;166(2):157-63
references.pmid: 21969360
references.citation: Resuscitation. 2013 Feb;84(2):206-12
references.pmid: 23108239
references.citation: PLoS One. 2011 Mar 23;6(3):e17243
references.pmid: 21448266