title: Diabetes topics associated with engagement on Twitter.
abstract: BACKGROUND
  Social media are widely used by the general public and by public health and health care professionals. Emerging evidence suggests engagement with public health information on social media may influence health behavior. However, the volume of data accumulating daily on Twitter and other social media is a challenge for researchers with limited resources to further examine how social media influence health. To address this challenge, we used crowdsourcing to facilitate the examination of topics associated with engagement with diabetes information on Twitter.
  
  
  METHODS
  We took a random sample of 100 tweets that included the hashtag "#diabetes" from each day during a constructed week in May and June 2014. Crowdsourcing through Amazon's Mechanical Turk platform was used to classify tweets into 9 topic categories and their senders into 3 Twitter user categories. Descriptive statistics and Tweedie regression were used to identify tweet and Twitter user characteristics associated with 2 measures of engagement, "favoriting" and "retweeting."
  
  
  RESULTS
  Classification was reliable for tweet topics and Twitter user type. The most common tweet topics were medical and nonmedical resources for diabetes. Tweets that included information about diabetes-related health problems were positively and significantly associated with engagement. Tweets about diabetes prevalence, nonmedical resources for diabetes, and jokes or sarcasm about diabetes were significantly negatively associated with engagement.
  
  
  CONCLUSIONS
  Crowdsourcing is a reliable, quick, and economical option for classifying tweets. Public health practitioners aiming to engage constituents around diabetes may want to focus on topics positively associated with engagement.
authors.affiliation: Washington University in St. Louis, 1 Brookings Dr, CB 1196, St. Louis, MO, 63130. Email: harrisj@wustl.edu.
authors.forename: Jenine K
authors.initials: JK
authors.lastname: Harris
authors.affiliation: Brown School, Washington University in St. Louis, St. Louis, Missouri.
authors.forename: Adelina
authors.initials: A
authors.lastname: Mart
authors.affiliation: Brown School, Washington University in St. Louis, St. Louis, Missouri.
authors.forename: Sarah
authors.initials: S
authors.lastname: Moreland-Russell
authors.affiliation: Brown School, Washington University in St. Louis, St. Louis, Missouri.
authors.forename: Charlene A
authors.initials: CA
authors.lastname: Caburnay
mesh_terms: D057218:Blogging / Q000706:statistics & numerical data; D003256:Community Participation / Q000706:statistics & numerical data*; D054626:Consumer Health Information; D063045:Crowdsourcing / Q000379:methods*; D003625:Data Collection; D003626:Data Display; D003920:Diabetes Mellitus / Q000517:prevention & control; D015438:Health Behavior; D006801:Humans; D033181:Information Dissemination; D016247:Information Storage and Retrieval / Q000706:statistics & numerical data; D020407:Internet / Q000706:statistics & numerical data*; D016012:Poisson Distribution; D012044:Regression Analysis; D015203:Reproducibility of Results; D061108:Social Media; D060756:Social Networking; D012985:Software Design; D009626:Terminology as Topic