Abstract
Despite the range of studies examining the relationship between mental health and social media data, not all prior studies have validated the social media markers against “ground truth”, or validated psychiatric information, in general community samples. Instead, researchers have approximated psychiatric diagnosis using user statements such as “I have been diagnosed as X”. Without “ground truth”, the value of predictive algorithms is highly questionable and potentially harmful. In addition, for social media data, whilst linguistic features have been widely identified as strong markers of mental health disorders, little is known about non-textual features on their links with the disorders. The current work is a longitudinal study during which participants’ mental health data, consisting of depression and anxiety scores, were collected fortnightly with a validated, diagnostic, clinical measure. Also, datasets with labels relevant to mental health scores, such as emotional scores, are also employed to improve the performance in prediction of mental health scores. This work introduces a deep neural network-based method integrating sub-networks on predicting affective scores and mental health outcomes from images. Experimental results have shown that in the both predictions of emotion and mental health scores, (1) deep features majorly outperform handcrafted ones and (2) the proposed network achieves better performance compared with separate networks.
H. Nguyen and V. Nguyen—contributed equally
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Andrews, G., Issakidis, C., Sanderson, K., Corry, J., Lapsley, H.: Utilising survey data to inform public policy: comparison of the cost-effectiveness of treatment of ten mental disorders. Br. J. Psychiatry 184(6), 526–533 (2004)
Burgess, P.M., Pirkis, J.E., Slade, T.N., Johnston, A.K., Meadows, G.N., Gunn, J.M.: Service use for mental health problems: findings from the 2007 national survey of mental health and wellbeing. Aust. New Zealand J. Psychiatry 43(7), 615–623 (2009)
Christiana, J.M.: Duration between onset and time of obtaining initial treatment among people with anxiety and mood disorders: an international survey of members of mental health patient advocate groups. Psychol. Med. 30(3), 693–703 (2000)
De Choudhury, M., Counts, S., Horvitz, E.: Social media as a measurement tool of depression in populations. In: Proceedings of the Annual ACM Web Science Conference, pp. 47–56. ACM (2013)
Giles, J.: Making the links. Nature 488(7412), 448–450 (2012)
Gosling, J.A.: The GoodNight study - online CBT for insomnia for the indicated prevention of depression: study protocol for a randomised controlled trial. Trials 15(1), 56 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2015)
Kroenke, K., Spitzer, R.L., Williams, J.B.W.: The PHQ-9. J. Gen. Internal Med. 16(9), 606–613 (2001)
Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): affective ratings of pictures and instruction manual. Technical report, NIMH Center for the Study of Emotion and Attention (2005)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Ledford, H.: If depression were cancer. Nature 515(7526), 182–184 (2014)
Manikonda, L., De Choudhury, M.: Modeling and understanding visual attributes of mental health disclosures in social media. In: Proceedings of the Conference on Human Factors in Computing Systems, CHI 2017, pp. 170–181 (2017)
Mathers, C.: The global burden of disease: 2004 update. World Health Organization (2008)
Nimrod, G.: Online depression communities: members’ interests and perceived benefits. Health Commun. 28(5), 425–434 (2013)
Olfson, M., Kessler, R.C., Berglund, P.A., Lin, E.: Psychiatric disorder onset and first treatment contact in the United States and Ontario. Am. J. Psychiatry 155(10), 1415–1422 (1998)
Patrick, K.: Gaining insight from patient and person-generated real world/real time data. In: Medicine 2.0 Conference (2013)
Reece, A.G., Danforth, C.M.: Instagram photos reveal predictive markers of depression. EPJ Data. Sci. 6(1), 15 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations (2015)
Spitzer, R.L., Kroenke, K., Williams, J.B.W., Löwe, B.: A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch. Internal Med. 166(10), 1092–1097 (2006)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (2014)
Thompson, A., Issakidis, C., Hunt, C.: Delay to seek treatment for anxiety and mood disorders in an Australian clinical sample. Behav. Change 25(2), 71–84 (2008)
Thornicroft, G., Sartorius, N.: The course and outcome of depression in different cultures: 10-year follow-up of the WHO collaborative study on the assessment of depressive disorders. Psychol. Med. 23(4), 1023–1032 (1993)
van Spijker, B.A.J.: Reducing suicidal thoughts in the Australian general population through web-based self-help: study protocol for a randomized controlled trial. Trials 16(1), 62 (2015)
Woolf, S.H.: The meaning of translational research and why it matters. JAMA 299(2), 211–213 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, H. et al. (2018). Jointly Predicting Affective and Mental Health Scores Using Deep Neural Networks of Visual Cues on the Web. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11234. Springer, Cham. https://doi.org/10.1007/978-3-030-02925-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-02925-8_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02924-1
Online ISBN: 978-3-030-02925-8
eBook Packages: Computer ScienceComputer Science (R0)