Temporal variability of problem drinking on Twitter


Twitter is a micro-blogging application, which is commonly used as a way for individuals to maintain social connections. Social scientists have also begun using Twitter as a data source for understanding more about human interactions. There is very little research about Twitter’s utility for monitoring health related attitudes, beliefs and behaviors. The purpose of this study was to examine the extent to which individuals tweeted about problem drinking, and to identify if such tweets corresponded with time periods when problem drinking was likely to occur. Data from this study came from tweets originating in one of 9 randomly selected states, one from each of the nine census geographies in the US. Twitter’s API was used to collect tweets during the month of October 2010, and again during the time period surrounding New Year’s Eve 2010. Keywords were selected which indicated problem-drinking behaviors, and tweets were coded for the presence or absence of these keywords. Twitter users were most likely to tweet about problem drinking on Friday, Saturday and Sunday during the hours from 10 pm to 2 am. Tweets originating during the New Year’s Eve holiday (0.53%) were twice as common when compared to tweets during weekends in the month of October (0.34%). Twitter may be a valid data source for social scientists, given that tweets about problem drinking corresponded with expected time periods of actual problem drinking. Furthermore, tweets that mention problem drinking may be problematic for public health if they establish incorrect normative beliefs that such behaviors are acceptable and expected. Social norms interventions may be an effective tool in correcting misperceptions related to problem drinking by informing Twitter followers that problem drinking behaveiors are not normative.

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West, J. , Hall, P. , Hanson, C. , Prier, K. , Giraud-Carrier, C. , Neeley, E. and Barnes, M. (2012) Temporal variability of problem drinking on Twitter. Open Journal of Preventive Medicine, 2, 43-48. doi: 10.4236/ojpm.2012.21007.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Webb, T., Joseph, J., Yardley, L. and Michie, S. (2010) Using the Internet to promote health behavior change: A systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of Medical Internet Research, 12, e4. doi:10.2196/jmir.1376
[2] Java, A., Song, X., Finin, T. and Tseng, B. (2007) Why we Twitter: Understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, San Jose, 56-65.
[3] Smith, C. (2011) Twitter user statistics show stunning growth. The Huffington Post. http://www.huffingtonpost.com/2011/03/14/twitter-user-statistics_n_835581.html.
[4] Ajzen, I. and Fishbein, M. (1975) Belief, attitude, intention and behavior: An introduction to theory and research. Addision-Wesley, Reading.
[5] Montano, D.E. and Kasprzyk, D. (2008) Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. In: Glanz, K., Rimer, B.K. and Viswanath, K. Eds., Health Behavior and Health Education, Jossey-Bass, San Francisco, 67-96.
[6] Krishnamurthy, B., Gill, P. and Arlitt, M. (2008) A few chirps about Twitter. In Proceedings of the 1st workshop on Online social networks, Seattle, 19-24. doi:10.1145/1397735.1397741
[7] Zhao, D. and Rosson, M.B. (2009) How and why people Twitter: The role that micro-blogging plays in informal communication at work. In proceedings of the ACM 2009 international conference on Supporting group work, Sanibel Island, 243-252.
[8] Savage, N. (2011) Twitter as medium and message. Communications of the ACM, 54, 18-20.
[9] Del Boca, F.K., Darkes, J., Greenbaum, P.E. and Goldman, M.S. (2004) Up close and personal: Temporal variability in the drinking of individual college students during their first year. Journal of Consulting and Clinical Psychology, 72, 155-164. doi:10.1037/0022-006X.72.2.155
[10] Mèkelè, P., Martikainen, P. and Nihtilè, E. (2005) Temporal variation in deaths related to alcohol intoxication and drink-ing. International Journal of Epidemiology, 34, 765-771. doi:10.1093/ije/dyi025
[11] Lemmens, P.H.H.M. and Knibbe, R. (1993) Seasonal variation in survey and sales estimates of alcohol consumption. Journal of Studies on Alcohol, 54, 157-163.
[12] Golder, S.A. and Macy, M.W. (2011) Diurnal and seasonal mood vary with work, sleep, and day length across diverse cultures. Science, 333, 1878-1881.
[13] Moreno, M.A., Christakis, D.A., Egan, K.G., Brockman, L.N. and Becker, T. (2011) Associations between displayed alcohol references on Facebook and problem drinking among college students. Archives of Pediatric and Adolescent Medicine, E1-E7.
[14] Moreno, M.A., Christakis, D.A., Egan, K.G., Jelenchick, L.A., Cox, E., Young, H., Villiard, H. and Becker, T. (2011) A pilot evaluation of associations between displayed depression references on Facebook and self-reported depression using a clinical scale. Journal of Behavioral Health Services & Research, 1-9. doi:10.1007/s11414-011-9258-7
[15] Moreno, M.A., Jelenchick, L.A., Egan, K.G., Cox, E., Young, H., Gannon, K.E. and Becker, T. (2011) Feeling bad on Facebook: Depression disclosures by college students on a social networking site. Depress and Anxiety, 28, 447-455. doi:10.1002/da.20805
[16] Jung, T., Shim, W. and Mantaro, T. (2010) Psychological reactance and effects of social norms messages among binge drinking college students. Journal of Alcohol and Drug Education, 54, 7-18.
[17] Centers for Disease Control and Prevention (2010) Alcohol and public health. http://www.cdc.gov/alcohol/
[18] Paul, M.J. and Dredze, M. (2011) You are what you tweet: Analyzing Twitter for public health. Proceedings of the fifth international conference on Weblogs and social media, Barcelona, 265-272.
[19] Prier, K.W., Smith, M.S., Giraud-Carrier, C. and Hanson, C.L. (2011) Identifying health-related topics in Twitter: An exploration of tobacco-related tweets as a test topic. In Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction, College Park, 18-25.
[20] Neighbors, C., Dillard, A.J., Lewis, M.A., Bergstrom, R.L. and Neil, T.A. (2006) Normative misperceptions and temporal precedence of perceived norms and drinking. Journal of Studies on Alcohol, 67, 290-299.
[21] Borsari, B. and Carey, K. (2003) Descriptive and injunctive norms in college drinking: A meta-analytic integration. Journal of Studies on Alcohol, 64, 331-341.
[22] The National Center on Addiction and Substance Abuse at Columbia University (2011) National Survey of American attitudes on substance abuse XVI: Teens and parents. http://www.casacolumbia.org/templates/NewsRoom.aspx?articleid=648&zoneid=51
[23] Berkowitz, A.D. (2004) An overview of the social norms approach. In: Lederman, L. and Stewart, L. Eds., Changing the Culture of College Drinking, Hampton Press, Creeskill, 193-214.
[24] Joinson, A.N. (2001) Self disclosure in computer mediated communication: The role of self awareness and visual anonymity. European Journal of Social Psychology, 31, 177-192. doi:10.1002/ejsp.36

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