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31-01-2019 | Original Article

#Sad: Twitter Content Predicts Changes in Cognitive Vulnerability and Depressive Symptoms

Auteurs: Maria P. Sasso, Annaleis K. Giovanetti, Anastasia L. Schied, Hugh H. Burke, Gerald J. Haeffel

Gepubliceerd in: Cognitive Therapy and Research | Uitgave 4/2019

Abstract

Research shows that social media networks can affect both the physical and mental health of its users. We hypothesized that social media would also be associated with cognitive vulnerability to depression. To test this hypothesis, we used a 3-month pre-post prospective longitudinal design with a sample of undergraduates (n = 105). Results showed that participants who had tweets with a “past focus” (as determined by LIWC software) were more likely to exhibit increases in cognitive vulnerability and depressive symptoms than participants who did not have tweets with a past focus. Increases in cognitive vulnerability were associated with increases in depressive symptoms. However, the effect of Twitter content on future depressive symptoms was not accounted for by increases in cognitive vulnerability. Rather, one’s past focus Twitter content had an effect on future depressive symptoms that was independent of its effect on future cognitive vulnerability levels. These results provide further support for the plasticity of cognitive vulnerability in early adulthood as well as corroborate emerging evidence for the association between social media and mental health risk factors.

volgende artikel Effects of Mindfulness-Based Cognitive Therapy on a Behavioural Measure of Rumination in Patients with Chronic, Treatment-Resistant Depression

Abramson, L. Y., Alloy, L. B., Hogan, M. E., Whitehouse, W. G., Donovan, P., Rose, D., … Raniere, D. (1999). Cognitive vulnerability to depression: Theory and evidence. Journal of Cognitive Psychotherapy, 13, 5–20.CrossRef

Abramson, L. Y., Metalsky, G. I., & Alloy, L. B. (1989). Hopelessness depression: A theory-based subtype of depression. Psychological Review, 96(2), 358–372. https://doi.org/10.1037/0033-295X.96.2.358.CrossRef

Alloy, L. B., Abramson, L. Y., Hogan, M. E., Whitehouse, W. G., Rose, D. T., Robinson, M. S., … Lapkin, J. B. (2000). The Temple-Wisconsin Cognitive Vulnerability to Depression (CVD) Project: Lifetime history of Axis I psychopathology in individuals at high and low cognitive vulnerability to depression. Journal of Abnormal Psychology, 109, 403–418. https://doi.org/10.1037/0021-843X.109.3.403.CrossRefPubMed

Alloy, L. B., Abramson, L. Y., Tashman, N. A., Berrebbi, D. S., Hogan, M. E., Whitehouse, W. G., … Morocco, A. (2001). Developmental origins of cognitive vulnerability to depression: Parenting, cognitive, and inferential feedback styles of the parents of individuals at high and low cognitive risk for depression. Cognitive Therapy and Research, 25, 397–423. https://doi.org/10.1023/A:1005534503148.CrossRef

Alloy, L. B., Abramson, L. Y., Whitehouse, W. G., Hogan, M. E., Panzarella, C., & Rose, D. T. (2006). Prospective incidence of first onsets and recurrences of depression in individuals at high and low cognitive risk for depression. Journal of Abnormal Psychology, 115, 145–156. https://doi.org/10.1037/0021843X.115.1.145.CrossRefPubMed

Beck, A. T., Rush, A. J., Shaw, B. F., & Emery, G. (1979). Cognitive therapy of depression. New York: Guilford.

Beck, A. T., Steer, R. A., & Garbin, M. G. (1988). Psychometric properties of the BDI: Twenty-five years of evaluation. Clinical Psychology Review, 8, 77–100. https://doi.org/10.1016/02727358(88)90050-5.CrossRef

Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computer Science, 2, 1–8. https://doi.org/10.1016/j.jocs.2010.12.007.CrossRef

Broniatowski, D. A., Paul, M. J., & Dredze, M. (2013). National and local influenza surveillance through Twitter: An analysis of the 2012–2013 influenza epidemic. PLoS ONE, 8, 1–8. https://doi.org/10.1371/journal.pone.0083672.CrossRef

Campbell, R. S., & Pennebaker, J. W. (2003). The secret life of pronouns: Flexibility in writing styles and physical health. Psychological Science, 14(1), 60–65. https://doi.org/10.1111/1467-9280.01419.CrossRefPubMed

Christakis, N. A., & Fowler, J. H. (2013). Social contagion theory: Examining dynamic social networks and human behavior. Statistics in Medicine, 32(4), 556–577. https://doi.org/10.1002/sim.5408.CrossRefPubMed

Clark, L. A., & Watson, D. (1991). Tripartite model of anxiety and depression: Psychometric evidence and taxonomic implications. Journal of Abnormal Psychology, 100(3), 316–336. https://doi.org/10.1037/0021-843X.100.3.316.CrossRefPubMed

Cole, D. A., Ciesla, J. A., Dallaire, D. H., Jacquez, F. M., Pineda, A. Q., LaGrange, B., … Felton, J. W. (2008). Emergence of attributional style and its relation to depressive symptoms. Journal of Abnormal Psychology, 117, 16–31. https://doi.org/10.1037/0021-843X.117.1.16.CrossRefPubMed

Conway, M., & O’Connor, D. (2016). Social media, big data, and mental health: Current advances and ethical implications. Current Opinion in Psychology, 9, 7–82. https://doi.org/10.1016/j.copsyc.2016.01.004.CrossRef

Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2016). The spreading of misinformation online. Proceedings of the National Academy of Sciences, 113, 554–559. https://doi.org/10.1073/pnas.1517441113.CrossRef

Eichstaedt, J. C., Schwartz, H. A., Ker, M. L., Park, G., Labarthe, D. R., Merchant, R. M. … Seligman, M. E. (2015). Psychological language on Twitter predicts county-level heart disease mortality. Psychological Science, 26(2), 159–169. https://doi.org/10.1177/0956797614557867.CrossRefPubMedPubMedCentral

Freifeld, C. C., Brownstein, J. S., Menone, C. M., Bao, W., Filice, R., Kass-Hout, T., & Dasgupta, N. (2014). Digital drug safety surveillance: monitoring pharmaceutical products in twitter. Drug Safety, 37, 343. https://doi.org/10.1007/s40264-014-0155-x.CrossRefPubMedPubMedCentral

Garber, J., & Flynn, C. (2001). Predictors of depressive cognitions in young adolescents. Cognitive Therapy and Research, 25, 353–376. https://doi.org/10.1023/A:1005530402239.CrossRef

Gray, R., Vitak, J., Easton, E. W., & Ellison, N. B. (2013). Examining social adjustment to college in the age of social media: Factors influencing successful transitions and persistence. Computers & Education, 67, 193–207. https://doi.org/10.1016/j.compedu.2013.02.021.CrossRef

Haeffel, G. J. (2010). Cognitive vulnerability to depressive symptoms in college students: A comparison of traditional, weakest-link, and flexibility operationalizations. Cognitive Therapy and Research, 34, 92–98. https://doi.org/10.1007/s10608-008-9224-8.CrossRef

Haeffel, G. J., Abramson, L. Y., Brazy, P., Shah, J., Teachman, B., & Nosek, B. (2007). Explicit and implicit cognition: A preliminary test of a dual-process theory of cognitive vulnerability. Behaviour Research and Therapy, 45, 1155–1167. https://doi.org/10.1016/j.brat.2006.09.003.CrossRefPubMed

Haeffel, G. J., Eastman, M., & Grigorenko, E. L. (2012). Using a cognitive endophenotype to identify risk genes for depression. Neuroscience Letters, 510, 10–13. https://doi.org/10.1016/j.neulet.2011.12.060.CrossRefPubMed

Haeffel, G. J., Gibb, B. E., Metalsky, G. I., Alloy, L. B., Abramson, L. Y., Hankin, B. L., … Swendsen, J. D. (2008). Measuring cognitive vulnerability to depression: Development and validation of the cognitive style questionnaire. Clinical Psychology Review, 28(5), 824–836. https://doi.org/10.1016/j.cpr.2007.12.001.CrossRefPubMed

Haeffel, G. J., & Hames, J. L. (2014). Cognitive vulnerability to depression can be contagious. Clinical Psychological Science., 2, 75–85. https://doi.org/10.1177/2167702613485075.CrossRef

Haeffel, G. J., & Vargas, I. (2011). Resilience to depressive symptoms: The buffering effects of enhancing cognitive style and positive life events. Journal of Behavior Therapy and Experimental Psychiatry, 42, 13–18. https://doi.org/10.1016/j.jbtep.2010.09.003.CrossRefPubMed

Hankin, B. L., Abramson, L. Y., Miller, N., & Haeffel, G. J. (2004). Cognitive vulnerability-stress theories of depression: Examining affective specificity in the prediction of depression versus anxiety in three prospective studies. Cognitive Therapy and Research, 28(3), 309–345. https://doi.org/10.1023/B:COTR.0000031805.60529.0d.CrossRef

Harvard, I. O. P. (2018). http://www.iop.harvard.edu/use-social-networking-technology.

Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis. New York: Guildford Press.

Internet Live Statistics (2018) http://www.internetlivestats.com/.

Jashinsky, J., Burton, S. H., Hanson, C. L., West, J., Giraud-Carrier, C., Barnes, M. D., & Argyle, T. (2014). Tracking suicide risk factors through Twitter in the US. Crisis, 35, 51–59. https://doi.org/10.1027/0227-5910/a000234.CrossRefPubMed

Kessler, R. C., Borges, G., & Walters, E. E. (1999). Prevalence of and risk factors for lifetime suicide attempts in the National Comorbidity Survey. Arch Gen Psychiatry, 56, 617–626. https://doi.org/10.1001/archpsyc.56.7.617.CrossRefPubMed

Klein, K., & Boals, A. (2001). Expressive writing can increase working memory capacity. Journal of Experimental Psychology: General, 130(3), 520–533. https://doi.org/10.1037//0096-344S.130.3.520.CrossRef

Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences of the United States of America, 111(24), 8788–8790. https://doi.org/10.1073/pnas.1320040111.CrossRefPubMedPubMedCentral

Kross, E., Verduyn, P., Demiralp, E., Park, J., Lee, D. S., Lin, N., Shablack, H., Jonides, J., & Ybarra, O. (2013). Facebook use predicts declines in subjective well-being in young adults. PLoS ONE, 8, e69841. https://doi.org/10.1371/journal.pone.0069841.CrossRefPubMedPubMedCentral

Lehart, A., Purcell, K., Smith, A., & Zickuhr, K. (2010). Social media & mobile internet use among teens and young adults. Pew Internet & American Life Project. http://pewinternet.org/Reports/2010/Social-Media-and-Young-Adults.aspx.

Levenson, J. C., Shensa, A., Sidani, J. E., Colditz, J. B., & Primack, B. A. (2016). The association between social media use and sleep disturbance among young adults. Preventative Medicine, 85, 36–41. https://doi.org/10.1016/j.ypmed.2016.01.001.CrossRef

Metalsky, G. I., & Joiner, T. E. (1992). Vulnerability to depressive symptomatology: A prospective test of the diathesis-stress and causal mediation components of the hopelessness theory of depression. Journal of Personality and Social Psychology, 63, 667–675. https://doi.org/10.1037/0022-3514.63.4.667.CrossRefPubMed

Metaxas, P. T., & Mustafaraj, E. (2012). Social media and elections. Science, 338, 472–473. https://doi.org/10.1126/science.1230456.CrossRefPubMed

Mezulis, A. H., Hyde, J. S., & Abramson, L. Y. (2006). The developmental origins of cognitive vulnerability to depression: Temperament, parenting, and negative life events in childhood as contributors to negative cognitive style. Developmental Psychology, 42(6), 1012–1025. https://doi.org/10.1037/0012-1649.42.6.1012.CrossRefPubMed

Nolen-Hoeksema, S. (1991). Responses to depression and their effects on the duration of depressive episodes. Journal of Abnormal Psychology, 100, 569–582. https://doi.org/10.1037//0021-843X.100.4.569.CrossRefPubMed

Nolen-Hoeksema, S., Girgus, J. S., & Seligman, M. E. P. (1992). Predictors of consequences of childhood depressive symptoms: A 5-year longitudinal study. Journal of Abnormal Psychology, 101, 405–422. https://doi.org/10.1037/0021-843X.101.3.405.CrossRefPubMed

Nolen-Hoeksema, S., & Morrow, J. (1991). A prospective study of depression and posttraumatic stress symptoms after a natural disaster: The 1989 Loma Prieta earthquake. Journal of Personality and Social Psychology, 61, 115–121. https://doi.org/10.1037/0022-3514.61.1.115.CrossRefPubMed

Reece, A. G., & Danforth, C. M. (2017). Instagram photos reveal predictive markers of depression. EPJ Data Science, 6, 15. https://doi.org/10.1140/epjds/s13688-017-0110-z.CrossRef

Reece, A. G., Reagan, A. J., Lix, K. L., Dodds, P. S., Danforth, C. M., & Langer, E. J. (2017). Forecasting the onset and course of mental illness with Twitter data. Nature, 7, 13006. https://doi.org/10.1038/s41598-017-12961-9.CrossRef

Romens, S. E., Abramson, L. Y., & Alloy, L. B. (2009). High and low cognitive risk for depression: Stability from late adolescence to early adulthood. Cognitive Therapy and Research, 33, 480–498. https://doi.org/10.1007/s10608-008-9219-5.CrossRefPubMedPubMedCentral

Rude, S. S., Gortner, E. M., & Pennebaker, J. W. (2004). Language use of depressed and depression-vulnerable college students. Cognition and Emotion, 18(8), 1121–1133. https://doi.org/10.1080/02699930441000030.CrossRef

Seligman, M. E., Peterson, C., Kaslow, N. J., Tanenbaum, R. L., Alloy, L. B., & Abramson, L. Y. (1984). Attributional style and depressive symptoms among children. Journal of Abnormal Psychology, 93(2), 235–238. https://doi.org/10.1037//0021-843X.93.2.235.CrossRefPubMed

Smith, L. G. E., McGarty, C., & Thomas, E. F. (2018). After Aylan Kurdi: How tweeting about death, threat, and harm predict increased expressions of solidarity with refugees over time. Psychological Science, 29, 623–634. https://doi.org/10.1177/0956797617741107.CrossRefPubMed

Spasojevic, J., & Alloy, L. B. (2001). Rumination as a common mechanism relating depressive risk factors to depression. Emotion, 1, 25–37. https://doi.org/10.1037/1528-3542.1.1.25.CrossRefPubMed

Stark, K. D., Schmidt, K. L., & Joiner, T. E. (1996). Cognitive triad: relationship to depressive symptoms, parents’ cognitive triad, and percieved parental messages. Journal of Abnormal Psychology, 24(5), 615–631. https://doi.org/10.1007/BF01670103.CrossRef

Statista. (2017). Statista—The statistics portal for market data, market research and market studies. Retrieved December 30, 2017, from, http://www.statista.com/.

Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54. https://doi.org/10.1177/0261927X09351676.CrossRef

Treynor, W., Gonzalez, R., & Nolen-Hoeksema, S. (2003). Rumination reconsidered: A psychometric analysis. Cognitive Therapy and Research, 27, 247–259. https://doi.org/10.1023/A:1023910315561.CrossRef

Turner, J. C., Leno, E. V., & Keller, A. (2013). Causes of mortality among American college students: A pilot study. College Student Psychotherapy, 27, 31–42. https://doi.org/10.1080/87568225.2013.739022.CrossRef

Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6, 3–17. https://doi.org/10.1177/2167702617723376.CrossRef

Valente, M. J., & MacKinnon, D. P. (2017). Comparing models of change to estimate the mediated effect in the pretest-posttest control group design. Structural Equation Modeling, 24, 428–450. https://doi.org/10.1080/10705511.2016.1274657.CrossRefPubMedPubMedCentral

World Health Organization. (2004). The Global Burden of Disease: 2004 Update (Part 3). Retrieved from http://www.who.int/healthinfo/global_burden_disease/GBD_report_2004.update_full.pdf.

Titel: #Sad: Twitter Content Predicts Changes in Cognitive Vulnerability and Depressive Symptoms
Auteurs: Maria P. Sasso
Annaleis K. Giovanetti
Anastasia L. Schied
Hugh H. Burke
Gerald J. Haeffel
Publicatiedatum: 31-01-2019
Uitgeverij: Springer US
Gepubliceerd in: Cognitive Therapy and Research / Uitgave 4/2019
Print ISSN: 0147-5916
Elektronisch ISSN: 1573-2819
DOI: https://doi.org/10.1007/s10608-019-10001-6

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#Sad: Twitter Content Predicts Changes in Cognitive Vulnerability and Depressive Symptoms

Abstract

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