research-article

Characterizing and predicting postpartum depression from shared facebook data

Authors:

Munmun De Choudhury,

Eric J. Horvitz,

Aaron HoffAuthors Info & Claims

CSCW '14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing

Pages 626 - 638

https://doi.org/10.1145/2531602.2531675

Published: 15 February 2014 Publication History

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.

References

[1]

Beck, C.T. (1998). A checklist to identify women at risk for developing postpartum depression. Journal of Obstetric, Gynecologic, & Neonatal Nursing, 27, 39--46.

[2]

Beck, C.T. (2001). Predictors of Postpartum Depression: An Update. Nursing Research, 50 (5), 275--285.

[3]

Burke, M., Kraut, R., & Marlow, C. (2011). Social capital on Facebook: Differentiating uses and users. In Proc. CHI 2011. 571--580.

Digital Library

[4]

De Choudhury, M., Counts, S., & Horvitz, E. (2013). Major Life Changes and Behavioral Markers in Social Media: Case of Childbirth. In Proc. CSCW 2013.

Digital Library

[5]

De Choudhury, M., Counts, S., & Horvitz, E. (2013). Predicting Postpartum Changes in Emotion and Behavior via Social Media. In Proc. CHI 2013.

Digital Library

[6]

De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting Depression via Social Media. In Proc. ICWSM 2013.

[7]

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.

[8]

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.

Digital Library

[9]

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.

[10]

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.

[11]

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.

[12]

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.

[13]

Gibson, L. & Hanson, V.L. "Digital Motherhood": How Does Technology Support New Mothers? In Proc. CHI 20

Digital Library

[14]

Golder, S. A., & Macy, M. W. (2011). Diurnal and Seasonal Mood Vary with Work, Sleep and Daylength Across Diverse Cultures. Science. 30 Sep 2011.

[15]

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.

Digital Library

[16]

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.

[17]

Hamilton, J. D. (1994). Time Series Analysis (Vol. 2). Princeton: Princeton University Press.

[18]

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.

Digital Library

[19]

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.

Digital Library

[20]

Kroenke K, Spitzer RL, Williams JB (2001). The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 16(9):606.

[21]

McCullagh, P., and J. A. Nelder. Generalized Linear Models. New York: Chapman & Hall, 1990.

[22]

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.

[23]

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.

[24]

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.

[25]

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.

[26]

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.

Digital Library

[27]

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.

Digital Library

[28]

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.

[29]

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.

[30]

O'Hara, M.W. (1995). Postpartum Depression: Causes and Consequences. New York: Springer-Verlag.

[31]

Oxman T.E., Rosenberg S.D., & Tucker G.J. (1982). The language of paranoia. American J. Psychiatry 139:275--82.

[32]

Park, M., McDonald D., & Cha, M. (2013). Perception Differences between the Depressed and Non-depressed Users in Twitter. In Proc. ICWSM 2013.

[33]

Paul, M., & Dredze, M. (2011). You are what you tweet: Analyzing Twitter for public health. In Proc. ICWSM 2011.

[34]

Paykel, E. S., & Weissman, M. M. (1973). Social adjustment and depression: a longitudinal study. Archives of General Psychiatry, 28(5), 659.

[35]

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.

[36]

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.

[37]

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.

[38]

Rabkin, J. G.; & Struening, E. L. 1976. Life events, stress, and illness. Science, 194(4268), 1013--1020.

[39]

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.

[40]

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.

[41]

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.

[42]

Rosenquist, J. N., Fowler, J. H., & Christakis, N. A. (2010). Social network determinants of depression. Molecular psychiatry, 16(3), 273--281.

[43]

Rude, S.; Gortner, E.; & Pennebaker, J. 2004. Language use of depressed and depression-vulnerable college students. Cognition and Emotion, 1121--1133.

[44]

Sadilek, A., Kautz, H., & Silenzio, V. (2012). Modeling Spread of Disease from Social Interactions. In Proc. ICSWM 2011.

[45]

Schoenebeck, S.Y. The Secret Life of Online Moms: Anonymity and Disinhibition on YouBeMom.com. In Proc. ICWSM 2013.

[46]

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.

[47]

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.

[48]

Tarkka, M.-T. & Paunonen, M. (1996). Social support and its impact on mothers--experiences of childbirth. Journal of Advanced Nursing, 23: 70--75.

Cited By

Progga FRubya S(2025)Women's Perspectives and Challenges in Adopting Perinatal Mental Health TechnologiesProceedings of the ACM on Human-Computer Interaction10.1145/37012179:1(1-30)Online publication date: 10-Jan-2025
https://dl.acm.org/doi/10.1145/3701217
Alvarado Garcia AYang TMiceli M(2025)What Knowledge Do We Produce from Social Media Data and How?Proceedings of the ACM on Human-Computer Interaction10.1145/37012169:1(1-45)Online publication date: 10-Jan-2025
https://dl.acm.org/doi/10.1145/3701216
Dalal SJain SDave M(2025)Review of Advancements in Depression Detection Using Social Media DataIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.344862412:1(77-100)Online publication date: Mar-2025
https://doi.org/10.1109/TCSS.2024.3448624
Show More Cited By

Index Terms

Characterizing and predicting postpartum depression from shared facebook data
1. Human-centered computing
  1. Collaborative and social computing
  2. Human computer interaction (HCI)
2. Information systems
  1. Information retrieval
  2. Information storage systems

Recommendations

Social media as a measurement tool of depression in populations
WebSci '13: Proceedings of the 5th Annual ACM Web Science Conference

Depression is a serious and widespread public health challenge. We examine the potential for leveraging social media postings as a new type of lens in understanding depression in populations. Information gleaned from social media bears potential to ...
Major life changes and behavioral markers in social media: case of childbirth
CSCW '13: Proceedings of the 2013 conference on Computer supported cooperative work

We explore the harnessing of social media as a window on changes around major life events in individuals and larger populations. We specifically examine patterns of activity, emotional, and linguistic correlates for childbirth and postnatal course. ...
Predicting postpartum changes in emotion and behavior via social media
CHI '13: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

We consider social media as a promising tool for public health, focusing on the use of Twitter posts to build predictive models about the forthcoming influence of childbirth on the behavior and mood of new mothers. Using Twitter posts, we quantify ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CSCW '14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing

February 2014

1600 pages

ISBN:9781450325400

DOI:10.1145/2531602

General Chairs:
Susan Fussell
Cornell University
,
Wayne Lutters
University of Maryland, Baltimore County
,
Program Chairs:
Meredith Ringel Morris
Microsoft Research
,
Madhu Reddy
Penn State University

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 February 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CSCW'14

Sponsor:

SIGCHI

CSCW'14: Computer Supported Cooperative Work

February 15 - 19, 2014

Maryland, Baltimore, USA

Acceptance Rates

CSCW '14 Paper Acceptance Rate 134 of 497 submissions, 27%;

Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

Upcoming Conference

CSCW '25

Sponsor:
sigchi

Computer-Supported Cooperative Work and Social Computing

October 18 - 22, 2025

Bergen , Norway

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

238
Total Citations
View Citations
2,789
Total Downloads

Downloads (Last 12 months)187
Downloads (Last 6 weeks)12

Reflects downloads up to 25 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Progga FRubya S(2025)Women's Perspectives and Challenges in Adopting Perinatal Mental Health TechnologiesProceedings of the ACM on Human-Computer Interaction10.1145/37012179:1(1-30)Online publication date: 10-Jan-2025
https://dl.acm.org/doi/10.1145/3701217
Alvarado Garcia AYang TMiceli M(2025)What Knowledge Do We Produce from Social Media Data and How?Proceedings of the ACM on Human-Computer Interaction10.1145/37012169:1(1-45)Online publication date: 10-Jan-2025
https://dl.acm.org/doi/10.1145/3701216
Dalal SJain SDave M(2025)Review of Advancements in Depression Detection Using Social Media DataIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.344862412:1(77-100)Online publication date: Mar-2025
https://doi.org/10.1109/TCSS.2024.3448624
Pavez JAllende H(2024)A Hybrid System Based on Bayesian Networks and Deep Learning for Explainable Mental Health DiagnosisApplied Sciences10.3390/app1418828314:18(8283)Online publication date: 14-Sep-2024
https://doi.org/10.3390/app14188283
Merhbene GPuttick AKurpicz-Briki M(2024)Investigating machine learning and natural language processing techniques applied for detecting eating disorders: a systematic literature reviewFrontiers in Psychiatry10.3389/fpsyt.2024.131952215Online publication date: 26-Mar-2024
https://doi.org/10.3389/fpsyt.2024.1319522
Lin YLi NHuang WEcsedy KFeinberg MTeti DCarroll J(2024)"Ultimately We're Together": Understanding New Parents' Experiences of Co-parentingProceedings of the ACM on Human-Computer Interaction10.1145/36870188:CSCW2(1-25)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687018
Noh HGo JSong SKim SKang Y(2024)Investigating the Possibility of Using an AR Mask to Support Online Psychological CounselingProceedings of the ACM on Human-Computer Interaction10.1145/36373558:CSCW1(1-33)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637355
Lu XPowell JAgapie EChen YEpstein D(2024)Examining the Social Aspects of Pregnancy Tracking ApplicationsProceedings of the ACM on Human-Computer Interaction10.1145/36373288:CSCW1(1-30)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637328
Raturi AJoshi KAnupriya Jain PGupta VMeena J(2024)Hate Speech Detection System using Machine Learning Algorithms2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT)10.1109/InCACCT61598.2024.10551015(446-451)Online publication date: 2-May-2024
https://doi.org/10.1109/InCACCT61598.2024.10551015
Wang WBlackburn KThompson RBajaj KPedler RFucci K(2024)Trauma Isn’t One Size Fits All: How Online Support Communities Point to Different Diagnostic Criteria for C-PTSD and PTSDHealth Communication10.1080/10410236.2024.231434339:13(3272-3283)Online publication date: 11-Feb-2024
https://doi.org/10.1080/10410236.2024.2314343
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten