Towards Assessing Changes in Degree of Depression through Facebook
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H. Andrew Schwartz, Johannes Eichstaedt, Margaret L. Kern, Gregory Park, Maarten Sap, David Stillwell, Michal Kosinski, Lyle Ungar. Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. 2014.
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To guard against overfitting, no adjustments were made to the models, and thus this can be considered an evaluation of the models exactly as they were presented in their respective papers.
“Importantly, to guard against overfitting, no adjustments were made to the models, and thus this can be considered an evaluation of the models exactly as they were presented in their respective papers (Park et al., 2015; Schwartz et al., 2014).”
From World Trade Center responders in their own words: predicting PTSD symptom trajectories with AI-based language analyses of interviews (2023) · cites this paper as Schwartz et al., 2014
The “Degree of depression” section describes the original regression models and how they were trained. The cited paper cannot establish whether the citing authors made no adjustments to those models or whether their later evaluation used them exactly as presented; it does not address that overfitting precaution. (LLM, conf=0.78)
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Cites
An Introduction to the Five‐Factor Model and Its Applications
McCrae, R. R., & John, O. P. (1992) · Journal of Personality · doi:10.1111/j.1467-6494.1992.tb00970.x · Cited by 6,835 · see citing works
Loneliness matters: a theoretical and empirical review of consequences and mechanisms
Hawkley, L. C., & Cacioppo, J. T. (2010) · Annals of Behavioral Medicine · doi:10.1007/s12160-010-9210-8 · Cited by 4,782 · see citing works
The Epidemiology of Depression Across Cultures
Kessler, R. C., & Bromet, E. J. (2013) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-031912-114409 · Cited by 2,657 · see citing works
Comorbidity of anxiety and unipolar mood disorders
Mineka, S., Watson, D., & Clark, L. A. (1998) · Annual Review of Psychology · doi:10.1146/annurev.psych.49.1.377 · Cited by 1,573 · see citing works
Lichtman, J. H., Froelicher, E. S., Blumenthal, J. A., Carney, R. M., Doering, L. V., Frasure-Smith, N., Freedland, K. E., Jaffe, A. S., Leifheit-Limson, E. C., Sheps, D. S., et al. (2014) · Circulation · doi:10.1161/cir.0000000000000019 · Cited by 1,208 · see citing works
Golden, R. N., Gaynes, B. N., Ekstrom, R. D., Hamer, R. M., Jacobsen, F. M., Suppes, T., Wisner, K. L., & Nemeroff, C. B. (2005) · American Journal of Psychiatry · doi:10.1176/appi.ajp.162.4.656 · Cited by 896 · see citing works
Personality, Well-Being, and Health*
Friedman, H. S., & Kern, M. L. (2014) · Annual Review of Psychology · doi:10.1146/annurev-psych-010213-115123 · Cited by 530 · cited 1× · see citing works
“…been related to a range of physical conditions, including asthma, cancer, cardiovascular disease, diabetes, and chronic pain (Kessler and Bromet, 2013), although the causal direction is confounded; it may be that other factors cause both depression and physical illness (Friedman and Kern, 2014).”
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Research works printed but not linked: 18
- Aaron T Beck, Calvin H Ward, Mock Mendelson, Jeremiah Mock, and JK Erbaugh. 1961. An inven- tory for measuring depression. Archives of general psychiatry, 4(6):561.
- Aartjan TF Beekman, DJH Deeg, J Van Limbeek, AW Braam, MZ De Vries, W Van Tilburg, et al. 1997. Criterion validity of the Center for Epi- demiologic Studies Depression scale (CES-D): re- sults from a community-based sample of older sub- jects in The Netherlands. Psychological medicine, 27(1):231-236.
- Dan G Blazer, Ronald C Kessler, and Marvin S Swartz. 1998. Epidemiology of recurrent major and minor depression with a seasonal pattern. The National Co- morbidity Survey. The British Journal of Psychia- try, 172(2):164-167.
- Munmun De Choudhury, Scott Counts, and Eric Horvitz. 2013a. Predicting postpartum changes in emotion and behavior via social media. In Pro- ceedings of the 2013 ACM annual conference on Human factors in computing systems, pages 3267- 3276. ACM.
- Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. 2013b. Predicting de- pression via social media. In AAAI Conference on Weblogs and Social Media.
- R Katikalapudi, Sriram Chellappan, Frances Mont- gomery, Donald Wunsch, and Karl Lutzen. 2012. Associating Internet usage with depressive behav- ior among college students. Technology and Society Magazine, IEEE, 31(4):73-80.
- Ronald C Kessler, Howard G Birnbaum, Victoria Shahly, Evelyn Bromet, Irving Hwang, Katie A McLaughlin, Nancy Sampson, Laura Helena An- drade, Giovanni de Girolamo, Koen Demyttenaere, et al. 2010. Age differences in the prevalence and co-morbidity of DSM-IV major depressive episodes: results from the WHO World Mental Health Survey Initiative. Depression and anxiety, 27(4):351-364.
- Raymond W Lam and Robert D Levitan. 2000. Patho- physiology of seasonal affective disorder: a review. Journal of Psychiatry and Neuroscience, 25(5):469.
- Andres Magnusson and Timo Partonen. 2005. Focus Points. CNS Spectr, 10(8):625-634.
- Peter Paul A Mersch, Hermine M Middendorp, An- toinette L Bouhuys, Domien GM Beersma, and Rut- ger H van den Hoofdakker. 1999. Seasonal affec- tive disorder and latitude: a review of the literature. Journal of affective disorders, 53(1):35-48.
- Gregory J Meyer, Stephen E Finn, Lorraine D Eyde, Gary G Kay, Kevin L Moreland, Robert R Dies, Elena J Eisman, Tom W Kubiszyn, and Geoffrey M Reed. 2001. Psychological testing and psycholog- ical assessment: A review of evidence and issues. American psychologist, 56(2):128-165.
- Saif M. Mohammad, Svetlana Kiritchenko, and Xiao- dan Zhu. 2013. NRC-Canada: Building the State- of-the-Art in Sentiment Analysis of Tweets. In Pro- ceedings of the seventh international workshop on Semantic Evaluation Exercises (SemEval-2013), At- lanta, Georgia, USA, June.
- Megan A Moreno, Lauren A Jelenchick, Katie G Egan, Elizabeth Cox, Henry Young, Kerry E Gannon, and Tara Becker. 2011. Feeling bad on Face- book: Depression disclosures by college students on a social networking site. Depression and anxiety, 28(6):447-455.
- Yair Neuman, Yohai Cohen, Dan Assaf, and Gabbi Kedma. 2012. Proactive screening for depression through metaphorical and automatic text analysis. Artificial intelligence in medicine, 56(1):19-25.
- Sungkyu Park, Sang Won Lee, Jinah Kwak, Meeyoung Cha, and Bumseok Jeong. 2013. Activities on Face- book Reveal the Depressive State of Users. Journal of medical Internet research, 15(10).
- Leora N Rosen, Steven D Targum, Michael Terman, Michael J Bryant, Howard Hoffman, Siegfried F Kasper, Joelle R Hamovit, John P Docherty, Betty Welch, and Norman E Rosenthal. 1990. Prevalence of seasonal affective disorder at four latitudes. Psy- chiatry research, 31(2):131-144.
- C Thompson. 2001. Evidence-based treatment. Sea- sonal affective disorder: practice and research, pages 151-158.
- Asa Westrin and Raymond W Lam. 2007. Seasonal affective disorder: a clinical update. Annals of Clin- ical Psychiatry, 19(4):239-246.
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Social Networking Sites, Depression, and Anxiety: A Systematic Review
Seabrook, E. M., Kern, M. L., & Rickard, N. S. (2016) · JMIR Mental Health · doi:10.2196/mental.5842 · Cited by 763 · see citing works
“As such, the language used in interactions on SNSs could become a target of interest, particularly as it has been shown to be sensitive in identifying individuals with depression [91,92,94,143].”
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Self-citation
- Authors
- Schwartz, H. A., Eichstaedt, J., Kern, M. L., Park, G., Sap, M., Stillwell, D., Kosinski, M., & Ungar, L.
- Affiliations
- University of Pennsylvania (US)
- University of Cambridge (GB)
- ORCID
- Venue
- Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality
- Published
- 2014
- Pages
- 118-125
- OpenAlex
- W2154887492
- Cited by
- 256
- References
- 7
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 15 · 2023: 36 · 2022: 35 · 2021: 30 · 2020: 34 · 2019: 28 · 2018: 22 · 2017: 19 · 2016: 13 · 2015: 8 · 2014: 2
- FWCI
- 27.49
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on Computational Linguistics and Clinical Psychology (CLPsych) (publishedVersion, cc-by)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- depression, Facebook, social media, sentiment analysis, mental health monitoring, longitudinal change detection
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