title: The painful tweet: text, sentiment, and community structure analyses of tweets pertaining to pain.
abstract: BACKGROUND
  Despite the widespread popularity of social media, little is known about the extent or context of pain-related posts by users of those media.
  
  
  OBJECTIVE
  The aim was to examine the type, context, and dissemination of pain-related tweets.
  
  
  METHODS
  We used content analysis of pain-related tweets from 50 cities to unobtrusively explore the meanings and patterns of communications about pain. Content was examined by location and time of day, as well as within the context of online social networks.
  
  
  RESULTS
  The most common terms published in conjunction with the term "pain" included feel (n=1504), don't (n=702), and love (n=649). The proportion of tweets with positive sentiment ranged from 13% in Manila to 56% in Los Angeles, CA, with a median of 29% across cities. Temporally, the proportion of tweets with positive sentiment ranged from 24% at 1600 to 38% at 2400, with a median of 32%. The Twitter-based social networks pertaining to pain exhibited greater sparsity and lower connectedness than did those social networks pertaining to common terms such as apple, Manchester United, and Obama. The number of word clusters in proportion to node count was greater for emotion terms such as tired (0.45), happy (0.43), and sad (0.4) when compared with objective terms such as apple (0.26), Manchester United (0.14), and Obama (0.25).
  
  
  CONCLUSIONS
  Taken together, our results suggest that pain-related tweets carry special characteristics reflecting unique content and their communication among tweeters. Further work will explore how geopolitical events and seasonal changes affect tweeters' perceptions of pain and how such perceptions may affect therapies for pain.
authors.affiliation: University of Florida College of Medicine, Department of Anesthesiology, Gainesville, FL, United States. ptighe@anest.ufl.edu.
authors.forename: Patrick J
authors.identifier: http://orcid.org/0000-0002-1881-9941
authors.initials: PJ
authors.lastname: Tighe
authors.forename: Ryan C
authors.identifier: http://orcid.org/0000-0001-8162-2159
authors.initials: RC
authors.lastname: Goldsmith
authors.forename: Michael
authors.identifier: http://orcid.org/0000-0001-6400-4789
authors.initials: M
authors.lastname: Gravenstein
authors.forename: H Russell
authors.identifier: http://orcid.org/0000-0002-1367-2813
authors.initials: HR
authors.lastname: Bernard
authors.forename: Roger B
authors.identifier: http://orcid.org/0000-0002-7699-8183
authors.initials: RB
authors.lastname: Fillingim
mesh_terms: D006801:Humans; D020407:Internet; D010146:Pain; D010679:Philippines; D061108:Social Media; D060756:Social Networking
keywords: Twitter messaging; emotions; social networks; text mining
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