title: Leveraging Big Data to Improve Health Awareness Campaigns: A Novel Evaluation of the Great American Smokeout.
abstract: BACKGROUND
  Awareness campaigns are ubiquitous, but little is known about their potential effectiveness because traditional evaluations are often unfeasible. For 40 years, the "Great American Smokeout" (GASO) has encouraged media coverage and popular engagement with smoking cessation on the third Thursday of November as the nation's longest running awareness campaign.
  
  
  OBJECTIVE
  We proposed a novel evaluation framework for assessing awareness campaigns using the GASO as a case study by observing cessation-related news reports and Twitter postings, and cessation-related help seeking via Google, Wikipedia, and government-sponsored quitlines.
  
  
  METHODS
  Time trends (2009-2014) were analyzed using a quasi-experimental design to isolate spikes during the GASO by comparing observed outcomes on the GASO day with the simulated counterfactual had the GASO not occurred.
  
  
  RESULTS
  Cessation-related news typically increased by 61% (95% CI 35-87) and tweets by 13% (95% CI -21 to 48) during the GASO compared with what was expected had the GASO not occurred. Cessation-related Google searches increased by 25% (95% CI 10-40), Wikipedia page visits by 22% (95% CI -26 to 67), and quitline calls by 42% (95% CI 19-64). Cessation-related news media positively coincided with cessation tweets, Internet searches, and Wikipedia visits; for example, a 50% increase in news for any year predicted a 28% (95% CI -2 to 59) increase in tweets for the same year. Increases on the day of the GASO rivaled about two-thirds of a typical New Year's Day-the day that is assumed to see the greatest increases in cessation-related activity. In practical terms, there were about 61,000 more instances of help seeking on Google, Wikipedia, or quitlines on GASO each year than would normally be expected.
  
  
  CONCLUSIONS
  These findings provide actionable intelligence to improve the GASO and model how to rapidly, cost-effectively, and efficiently evaluate hundreds of awareness campaigns, nearly all for the first time.
authors.affiliation: Graduate School of Public Health, San Diego State University Chula Vista, CA United States.
authors.forename: John W
authors.identifier: 0000-0002-8831-8691
authors.initials: JW
authors.lastname: Ayers
authors.affiliation: American Cancer Society Behavioral Research Center Atlanta, GA United States.
authors.forename: J Lee
authors.identifier: 0000-0002-6716-7695
authors.initials: JL
authors.lastname: Westmaas
authors.affiliation: University of California San Diego School of Medicine La Jolla, CA United States.
authors.forename: Eric C
authors.identifier: 0000-0001-9221-0336
authors.initials: EC
authors.lastname: Leas
authors.affiliation: Human Language Technology Center of Excellence Johns Hopkins University Baltimore, MD United States.
authors.forename: Adrian
authors.identifier: 0000-0003-3915-4085
authors.initials: A
authors.lastname: Benton
authors.affiliation: Bryn Mawr College Bryn Mawr College Philadelphia, PA United States.
authors.forename: Yunqi
authors.identifier: 0000-0002-5293-883X
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Human Language Technology Center of Excellence Johns Hopkins University Baltimore, MD United States.
authors.forename: Mark
authors.identifier: 0000-0002-0422-2474
authors.initials: M
authors.lastname: Dredze
authors.affiliation: Santa Fe InstituteSanta Fe, NMUnited States; New Mexico State UniversityLas Cruces, NMUnited States.
authors.forename: Benjamin M
authors.identifier: 0000-0002-5464-654X
authors.initials: BM
authors.lastname: Althouse
keywords: big data; evaluation; health communication; infodemiology; infoveillence; mass media; smoking cessation; social media; tobacco control; twitter