Munmun De Choudhury
Eric J. Horvitz
ABSTRACT
The birth of a child is a major milestone in the life of parents. We leverage Facebook data shared voluntarily by 165 new mothers as streams of evidence for characterizing their postnatal experiences. We consider multiple measures including activity, social capital, emotion, and linguistic style in participants' Facebook data in pre- and postnatal periods. Our study includes detecting and predicting onset of post-partum depression (PPD). The work complements recent work on detecting and predicting significant postpartum changes in behavior, language, and affect from Twitter data. In contrast to prior studies, we gain access to ground truth on postpartum experiences via self-reports and a common psychometric instrument used to evaluate PPD. We develop a series of statistical models to predict, from data available before childbirth, a mother's likelihood of PPD. We corroborate our quantitative findings through interviews with mothers experiencing PPD. We find that increased social isolation and lowered availability of social capital on Facebook, are the best predictors of PPD in mothers.
- Beck, C.T. (1998). A checklist to identify women at risk for developing postpartum depression. Journal of Obstetric, Gynecologic, & Neonatal Nursing, 27, 39--46.Google Scholar
Cross Ref
- Beck, C.T. (2001). Predictors of Postpartum Depression: An Update. Nursing Research, 50 (5), 275--285.Google ScholarCross Ref
- Burke, M., Kraut, R., & Marlow, C. (2011). Social capital on Facebook: Differentiating uses and users. In Proc. CHI 2011. 571--580. Google ScholarDigital Library
- De Choudhury, M., Counts, S., & Horvitz, E. (2013). Major Life Changes and Behavioral Markers in Social Media: Case of Childbirth. In Proc. CSCW 2013. Google ScholarDigital Library
- De Choudhury, M., Counts, S., & Horvitz, E. (2013). Predicting Postpartum Changes in Emotion and Behavior via Social Media. In Proc. CHI 2013. Google ScholarDigital Library
- De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting Depression via Social Media. In Proc. ICWSM 2013.Google Scholar
- Dennis, C., and Chung-Lee, L. Postpartum Depression Help-Seeking Barriers and Maternal Treatment Preferences: A Qualitative Systematic Review. Birth, (2006), 33(4), 323--331.Google Scholar
- Doherty, G., Coyle, D., & Sharry, J. (2012). Engagement with Online Mental Health Interventions: An Exploratory Clinical Study of a Treatment for Depression. In Proc. CHI 2012. Google ScholarDigital Library
- Ellison, N. B., Steinfield, C., & Lampe, C. (2011). Connection strategies: Social capital implications of Facebook-enabled communication practices. New Media & Society, 13(6), 873--892.Google ScholarCross Ref
- Fleming, A. S., Klein, E. and Corter, C. (1992). The Effects of a Social Support Group on Depression, Maternal Attitudes and Behavior in New Mothers. J. Child Psychology and Psychiatry, 33: 685--698.Google ScholarCross Ref
- Fleming, Alison S.; Ruble, Diane N.; Flett, Gordon L.; Shaul, David L. (1988). Postpartum adjustment in first-time mothers: Relations between mood, maternal attitudes, and mother-infant interactions. Developmental Psychology, vol 24(1), pp. 71--81.Google ScholarCross Ref
- Fujiwara, T., & Kawachi, I. (2008). A prospective study of individual-level social capital and major depression in the USA. Journal of Epidemiology and Community Health, 62(7), 627--633.Google ScholarCross Ref
- Gibson, L. & Hanson, V.L. "Digital Motherhood": How Does Technology Support New Mothers? In Proc. CHI 20 Google ScholarDigital Library
- Golder, S. A., & Macy, M. W. (2011). Diurnal and Seasonal Mood Vary with Work, Sleep and Daylength Across Diverse Cultures. Science. 30 Sep 2011.Google Scholar
- Gray, R., Ellison, N. B., Vitak, J., & Lampe, C. (2013, February). Who wants to know?: question-asking and answering practices among Facebook users. In Proc. CSCW 2013. 1213--1224. Google ScholarDigital Library
- Halligan, S.L., Murray, L., Martins, C., and Cooper, P.J. Maternal depression and psychiatric outcomes in adolescent offsprint: A 13-year longitudinal study. Journal of Affective Disorders 97 (2007), 145--154.Google Scholar
- Hamilton, J. D. (1994). Time Series Analysis (Vol. 2). Princeton: Princeton University Press.Google Scholar
- Hui, M., Ly, C., and Neustaedter, C. MammiBelli: Sharing Baby Activity Levels Between Expectant Mothers and Their Intimate Social Groups. In Proc. EA CHI 2012, 1649--1654. Google ScholarDigital Library
- Kientz, J. A., Arriaga, R. I., & Abowd, G. D. (2009). Baby Steps : Evaluation Of A System To Support Record- Keeping For Parents Of Young Children. In Proc. CHI 2009. 1713--1722. Google ScholarDigital Library
- Kroenke K, Spitzer RL, Williams JB (2001). The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 16(9):606.Google ScholarCross Ref
- McCullagh, P., and J. A. Nelder. Generalized Linear Models. New York: Chapman & Hall, 1990.Google Scholar
- McDaniel, B.T., Coyne, S.M., and Holmes, E.K. (2012) New Mothers and Media Use: Associations between Blogging, Social Networking, and Maternal Well-Being. Maternal and Child Health Journal, 16(7), 1509--17.Google ScholarCross Ref
- Moreno MA, Parks MR, & Zimmerman FJ, et al. (2009). Display of health risk behaviors on MySpace by adolescents: Prevalence and associations. Archives of Pediatrics and Adolescent Medicine.163(1):35--41.Google ScholarCross Ref
- Moreno MA, Jelenchick LA, & Egan KG, et al. (2011). Feeling bad on Facebook: Depression disclosures by college students on a social networking site. Depressions and Anxiety. 28(6):447--455.Google ScholarCross Ref
- Moreno, M. A., Christakis, D. A., Egan, K. G., Brockman, L. N., & Becker, T. (2011). Associations between displayed alcohol references on Facebook and problem drinking among college students. Archives of Pediatrics & Adolescent Medicine. 166(2):157--63.Google ScholarCross Ref
- Morris, M. R., Teevan, J., & Panovich, K. (2010). What do people ask their social networks, and why?: a survey study of status message Q&A behavior. In Proc. CHI 2012. Google ScholarDigital Library
- Newman M, Lauterbach D, Munson SA, Resnick P., Morris, M. (2011). It's not that I don't have problems, I'm just not putting them on Facebook: Challenges and Opportunities in Using Online Social Networks for Health. In Proc. CSCW 2011. Google ScholarDigital Library
- NielsenWire. Infographic: The Digital Lives of American Moms. http://www.nielsen.com/us/en/newswire/2012/digital-lives-of-american-moms.html May 11, 2012.Google Scholar
- O'Hara, M. W., Neunaber, D. J., & Zekoski, E. M. (1984). Prospective study of postpartum depression: prevalence, course, and predictive factors. Journal of abnormal psychology, 93(2), 158.Google ScholarCross Ref
- O'Hara, M.W. (1995). Postpartum Depression: Causes and Consequences. New York: Springer-Verlag.Google Scholar
- Oxman T.E., Rosenberg S.D., & Tucker G.J. (1982). The language of paranoia. American J. Psychiatry 139:275--82.Google ScholarCross Ref
- Park, M., McDonald D., & Cha, M. (2013). Perception Differences between the Depressed and Non-depressed Users in Twitter. In Proc. ICWSM 2013.Google Scholar
- Paul, M., & Dredze, M. (2011). You are what you tweet: Analyzing Twitter for public health. In Proc. ICWSM 2011.Google Scholar
- Paykel, E. S., & Weissman, M. M. (1973). Social adjustment and depression: a longitudinal study. Archives of General Psychiatry, 28(5), 659.Google ScholarCross Ref
- Pennebaker, J.W., Mehl, M.R., and Niederhoffer, K.G. (2002). Pyschological aspects of natural language use: Our words, ourselves. Annual Review of Psychology 54: 547--477.Google ScholarCross Ref
- Plantin, Lars, and Kristian Daneback. 2009. "Parenthood, information and support on the internet. A literature review of research on parents and professionals online." BMC Family Practice 10(1):34.Google ScholarCross Ref
- Prigerson, H. G., Monk, T. H., Reynolds, C. F., Begley, A., Houck, P. R., Bierhals, A. J., & Kupfer, D. J. (1995). Lifestyle regularity and activity level as protective factors against bereavement-related depression in later life. Depression, 3(6), 297--302.Google ScholarCross Ref
- Rabkin, J. G.; & Struening, E. L. 1976. Life events, stress, and illness. Science, 194(4268), 1013--1020.Google ScholarCross Ref
- Righetti-Veltema, M., Conne-Perréard, E., Bousquet, A., & Manzano, J. (1998). Risk factors and predictive signs of postpartum depression. Journal of affective disorders, 49(3), 167--180.Google ScholarCross Ref
- Robinson, M. S.; & Alloy, L. B. 2003. Negative cognitive styles and stress-reactive rumination interact to predict depression: A prospective study. Cognitive Therapy and Research, 27(3), 275--291.Google ScholarCross Ref
- Roeloffs, C., Sherbourne, C., Unützer, J., Fink, A., Tang, L., & Wells, K. B. (2003). Stigma and depression among primary care patients. General hospital psychiatry, 25(5), 311--315.Google Scholar
- Rosenquist, J. N., Fowler, J. H., & Christakis, N. A. (2010). Social network determinants of depression. Molecular psychiatry, 16(3), 273--281.Google Scholar
- Rude, S.; Gortner, E.; & Pennebaker, J. 2004. Language use of depressed and depression-vulnerable college students. Cognition and Emotion, 1121--1133.Google Scholar
- Sadilek, A., Kautz, H., & Silenzio, V. (2012). Modeling Spread of Disease from Social Interactions. In Proc. ICSWM 2011.Google Scholar
- Schoenebeck, S.Y. The Secret Life of Online Moms: Anonymity and Disinhibition on YouBeMom.com. In Proc. ICWSM 2013.Google Scholar
- Scott KD, Klaus PH, Klaus MH.(1999). The obstetrical and postpartum benefits of continuous support during childbirth. J Womens Health Gend Based Med. vol 8(10):1257--64.Google ScholarCross Ref
- Steinfeld, C., Ellison, N., Lampe, C. (2008). Social capital, self-esteem, and use of online social network sites: A longitudinal study. J. of Applied Developmental Psychology, 29, 434--445.Google ScholarCross Ref
- Tarkka, M.-T. & Paunonen, M. (1996). Social support and its impact on mothers--experiences of childbirth. Journal of Advanced Nursing, 23: 70--75.Google ScholarCross Ref
Index Terms
- Characterizing and predicting postpartum depression from shared facebook data
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