title: Using social media to monitor mental health discussions - evidence from Twitter.
abstract: OBJECTIVE
  Given the public health importance of communicating about mental illness and the growing use of social media to convey information, our goal was to develop an empirical model to identify periods of heightened interest in mental health topics on Twitter.
  
  
  METHODS
  We collected data on 176 million tweets from 2011 to 2014 with content related to depression or suicide. Using an autoregressive integrated moving average (ARIMA) data analysis, we identified deviations from predicted trends in communication about depression and suicide.
  
  
  RESULTS
  Two types of heightened Twitter activity regarding depression or suicide were identified in 2014: expected increases in response to planned behavioral health events, and unexpected increases in response to unanticipated events. Tweet volume following expected increases went back to the predicted level more rapidly than the volume following unexpected events.
  
  
  CONCLUSIONS
  Although ARIMA models have been used extensively in other fields, they have not been used widely in public health. Our findings indicate that our ARIMA model is valid for identifying periods of heightened activity on Twitter related to behavioral health. The model offers an objective and empirically based measure to identify periods of greater interest for timing the dissemination of credible information related to mental health.
  
  
  CONCLUSIONS
  Spikes in tweet volume following a behavioral health event often last for less than 2 days. Individuals and organizations that want to disseminate behavioral health messages on Twitter in response to heightened periods of interest need to take this limited time frame into account.
authors.affiliation: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration, Rockville, Maryland, USA.
authors.forename: Chandler
authors.initials: C
authors.lastname: McClellan
authors.affiliation: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration, Rockville, Maryland, USA.
authors.forename: Mir M
authors.initials: MM
authors.lastname: Ali
authors.affiliation: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration, Rockville, Maryland, USA.
authors.forename: Ryan
authors.initials: R
authors.lastname: Mutter
authors.affiliation: Behavioral Health and Criminal Justice Division, Social, Statistical, and Environmental Sciences, RTI International, Research Triangle Park, North Carolina, USA.
authors.forename: Larry
authors.initials: L
authors.lastname: Kroutil
authors.affiliation: Behavioral Health and Criminal Justice Division, Social, Statistical, and Environmental Sciences, RTI International, Research Triangle Park, North Carolina, USA.
authors.forename: Justin
authors.initials: J
authors.lastname: Landwehr
mesh_terms: D003142:Communication; D003863:Depression / Q000453:epidemiology; D006801:Humans; D008603:Mental Health / Q000706:statistics & numerical data*; D015233:Models, Statistical; D061108:Social Media / Q000706:statistics & numerical data*; D013405:Suicide / Q000706:statistics & numerical data
keywords: ARIMA; Twitter; health communication; mental health; social media