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A deep architecture for depression detection using posting, behavior, and living environment data

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Abstract

The World Health Organization (WHO) predicts that depression disorders will be widespread in the next 20 years. These disorders may affect a person’s general health and habits such as altered sleeping and eating patterns in addition to their interpersonal relationships. Early depression detection and prevention therefore becomes an important issue. To address this critical issue, we recruited 1453 individuals who use Facebook frequently and collected their Facebook data. We then propose an automatic depression detection approach, named Deep Learning-based Depression Detection with Heterogeneous Data Sources (D3-HDS), to predict the depression label of an individual by analyzing his/her living environment, behavior, and the posting contents in the social media. The proposed method employs Recurrent Neural Networks to compute the posts representation of each individual. The representations are then combined with other content-based, behavior and living environment features to predict the depression label of the individual with Deep Neural Networks. To our best knowledge, this is the first attempt that simultaneously considers all the content-based, behavior, and living environment features for depression detection. The experiment results on a real dataset show that the performance of our approach significantly outperforms the other baselines.

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Notes

  1. https://emedicine.medscape.com/article/286759-overview?src=refgatesrc1

  2. http://data.moi.gov.tw/MoiOD/Data/DataDetail.aspx?oid=67781E29-8AAD-46A9-A2C8-C3F339592C27

  3. http://data.gov.tw/node/13764

  4. http://www.cwb.gov.tw/V7/index.htm

  5. https://www.ptt.cc/bbs/index.html

References

  • Depression Fact Sheet, World Health Organization, http://www.who.int/mediacentre/factsheets/fs369/en/ (2012).

  • Major depressive episode among full-time college students and other young adults, aged 18 to 22 NSDUH report (2012).

  • American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5) american psychiatric pub.

  • Andalibi, N., Ozturk, P., Forte, A. (2015). Depression-related imagery on instagram. In ACM conference companion on computer supported cooperative work & social computing (pp. 231–234).

  • Arevian, G. (2007). Recurrent neural networks for robust real-world text classification. In IEEE/WIC/ACM international conference on web intelligence (pp. 326–329).

  • Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C. (2003). A neural probabilistic language model. Journal of Machine Learning Research, 3(Feb), 1137–1155.

    MATH  Google Scholar 

  • Brunborg, G.S., Mentzoni, R.A., Froyland, L.R. (2014). Is video gaming, or video game addiction, associated with depression, academic achievement, heavy episodic drinking, or conduct problems? Journal of Behavioral Addictions, 3(1), 27–32.

    Article  Google Scholar 

  • Cavazos-Rehg, P.A., Krauss, M.J., Sowles, S., Connolly, S., Rosas, C., Bharadwaj, M., Bierut, L.J. (2016). A content analysis of Depression-Related tweets. Computers in Human Behavior, 54, 351–357.

    Article  Google Scholar 

  • Cheng, C.M., Chen, H.C., Chan, Y.C., Su, Y.C., Tseng, C.C. (2013). Taiwan corpora of chinese emotions and relevant psychophysiological data-normative data for chinese jokes. Chinese Journal of Psychology, 55(4), 555–569.

    Google Scholar 

  • Cohn, J.F., Kruez, T.S., Matthews, I., Yang, Y., Nguyen, M.H., Padilla, M.T., Zhou, F., De la Torre, F. (2009). Detecting depression from facial actions and vocal prosody.. In Proceedings of affective computing and intelligent interaction and workshops (pp. 1–7).

  • Chen, K.J., & Liu, S. (1992). Word identification for mandarin chinese sentences. Computational Linguistics, 1, 101–107.

    Google Scholar 

  • Deng, L., & Yu, D. (2014). Deep learning: Methods and applications. Foundations and Trends in Signal Processing, 7(3–4), 197–387.

    Article  MathSciNet  Google Scholar 

  • De Choudhury, M., Gamon, M., Counts, S., Horvitz, E. (2013). Predicting depression via social media. AAAI Conference on Weblogs and Social Media (ICWSM), 13, 1–10.

    Google Scholar 

  • Elman, J.L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.

    Article  Google Scholar 

  • Galea, S., Ahern, J., Rudenstine, S., Wallace, Z., Vlahov, D. (2005). Urban built environment and depression: a multilevel analysis. Journal of Epidemiology & Community Health, 59(10), 822–827.

    Article  Google Scholar 

  • Grus, J. (2015). Data science from scratch: first principles with Python. O’Reilly Media Inc.

  • Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., Schmidhuber, J. (2009). A novel connectionist system for improved unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5, 31.

    Google Scholar 

  • Hsieh, Y., & Boland, J. (2010). Predicting processing difficulty in chinese syntactic ambiguity resolution: a parallel approach, LSA annual meeting, pp. 37-1-5.

  • Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J. (2001). Gradient flow in recurrent nets: the difficulty of learning long-term dependencies.

  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.

    Article  Google Scholar 

  • Ku, L.W., & Chen, H.H. (2007). Mining opinions from the web: beyond relevance retrieval. Journal of the Association for Information Science and Technology, 58(12), 1838–1850.

    Google Scholar 

  • Kingma, D., & Ba, J. (2014). Adam: A method for stochastic optimization, arXiv:1412.6980.

  • Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C. (2002). Text classification using string kernels. Journal of Machine Learning Research, 2, 419–444.

    MATH  Google Scholar 

  • Lai, S., Xu, L., Liu, K., Zhao, J. (2015). Recurrent convolutional neural networks for text classification. In AAAI conference on artificial intelligence (pp. 2267–2273).

  • Low, L.S.A., Maddage, N.C., Lech, M., Sheeber, L., Allen, N. (2010). Influence of acoustic low-level descriptors in the detection of clinical depression in adolescents. In Proceedings of acoustics speech and signal processing (ICASSP) (pp. 5154–5157).

  • Mikolov, T., Chen, K., Corrado, G., Dean, J. (2013). Efficient estimation of word representations in vector space. In: International conference on learning representations workshop.

  • Olsson, G., & Knorring, A.L. (1999). Adolescent depression: Prevalence in swedish High-School students. Acta Psychiatrica Scandinavica, 99(5), 324–331.

    Article  Google Scholar 

  • Radloff, L.S. (1977). The CES-d scale: a self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401.

    Article  Google Scholar 

  • Ramirez-Esparza, N., Chung, C.K., Kacewicz, E., Pennebaker, J.W. (2008). The psychology of word use in depression forums in english and in spanish: texting two text analytic approaches. In AAAI conference on Weblogs and social media (ICWSM).

  • Shapero, B.G., Black, S.K., Liu, R.T., Klugman, J., Bender, R.E., Abramson, L.Y., Alloy, L.B. (2014). Stressful life events and depression symptoms: The effect of childhood emotional abuse on stress reactivity. Journal of Clinical Psychology, 70(3), 209–223.

    Article  Google Scholar 

  • Shen, Y.C., Kuo, T.T., Yeh, I.N., Chen, T.T., Lin, S.D. (2013). Exploiting temporal information in a two-stage classification framework for content-based depression detection. In Pacific-Asia conference on knowledge discovery and data mining (pp. 276–288).

  • Sun, J. (2015). Jieba Chinese word segmentation tool, https://github.com/fxsjy/jieba.

  • Tekell, J.L., Hoffmann, R., Hendrickse, W., Greene, R.W., Rush, A.J., Armitage, R. (2005). High frequency EEG activity during sleep: Characteristics in schizophrenia and depression. Clinical EEG and Neuroscience, 36(1), 25–35.

    Article  Google Scholar 

  • Tung, C., & Lu, W. (2016). Analyzing Depression Tendency of Web Posts using An Event-driven Depression Tendency Warning Model. Artificial Intelligence in Medicine, 66, 53–62.

    Article  Google Scholar 

  • Tsugawa, S., Kikuchi, Y., Kishino, F., Nakajima, K., Itoh, Y., Ohsaki, H. (2015). Recognizing depression from twitter activity. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 3187–3196).

  • Wang, X., Zhang, C., Ji, Y., Sun, L., Wu, L., Bao, Z. (2013). A depression detection model based on sentiment analysis in micro-blog social network. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 201–213).

  • Weissman, M.M., Sholomskas, D., Pottenger, M., Prusoff, B., Locke, B. (1997). Assessing depressive symptoms in five psychiatric populations: a validation study American journal of Epidemiology.

  • Zlotnick, C., Kohn, R., Keitner, G., Della Grotta, S.A. (2000). The relationship between quality of interpersonal relationships and major depressive disorder: Findings from the national comorbidity survey. Journal of Affective Disorders, 59(3), 205–215.

    Article  Google Scholar 

  • Saravia, E., Chang, C., Lorenzo, R., Chen, Y. (2016). MIDAS: Mental Illness detection and analysis via social media. In IEEE/ACM international conference on advances in social networks analysis and mining.

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Authors and Affiliations

  1. Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu, Taiwan

    Min Yen Wu

  2. Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan

    Chih-Ya Shen

  3. Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan

    En Tzu Wang

  4. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

    Arbee L. P. Chen

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  1. Min Yen Wu
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Correspondence to Arbee L. P. Chen.

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Wu, M.Y., Shen, CY., Wang, E.T. et al. A deep architecture for depression detection using posting, behavior, and living environment data. J Intell Inf Syst 54, 225–244 (2020). https://doi.org/10.1007/s10844-018-0533-4

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  • DOI: https://doi.org/10.1007/s10844-018-0533-4

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