憂鬱症是一種常見且嚴重的精神障礙, 它會使人在日常生活中產生悲傷或絕望的情緒。隨著社群媒體的興起，大家會傾向在社群平台上表達自己的想法或情緒，而不是直接與家人或朋友訴說。而不同的社群平台有不同資料呈現的格式，使得巨量且多元的資料能被研究人員拿來進行數據分析。在我們的研究中，我們希望針對Instagram上偵測出有憂鬱症傾向的使用者。我們建立一個憂鬱症相關的字典來蒐集有憂鬱症傾向的使用者及非憂鬱症傾向的使用者當作我們的資料集。在預測模型方面，我們建構一個多模組系統，並使用使用者文章的圖片, 文字, 行為特徵來進行預測每篇文章的憂鬱分數。我們也提出了兩階段的偵測機制名為時間性偵測來偵測出憂鬱症使用者。實驗結果表明，我們提出的方法可以達到0.835 的F1-score，可以有效地偵測出在Instagram平台上可能有憂鬱症傾向的使用者，並提供了一個早期憂鬱症偵測工具，可以在憂鬱症病發前就先提早篩檢及預防。

Depression is a common and serious mental disorder that causes a person to have sad or hopeless feelings in his/her daily life. With the rapid development of social media, people tend to express their thoughts or emotions on the social platform. Different social platforms have various formats of data presentation, which makes huge and diverse data available for analysis by researchers. In our study, we aim to detect users with depressive tendency on Instagram. We created a depression dictionary for automatically collecting data of depressive and non-depressive users. In terms of the prediction model, we construct a multimodal system, which utilizes image, text and behavior features to predict the aggregated depression score of each post on Instagram. Considering the time interval between posts, we proposed a two-stage detection mechanism for detecting depressive users. Experimental results demonstrate that our proposed methods can achieve up to 0.835 F1-score for detecting depressive users. It can therefore serve as an early depression detector for a timely treatment before it becomes severe.

[1] M. Reddy, "Depression-the global crisis," Indian journal of psychological medicine, vol. 34, no. 3, p. 201, 2012.
[2] D. Hann, K. Winter, and P. Jacobsen, "Measurement of depressive symptoms in cancer patients: evaluation of the Center for Epidemiological Studies Depression Scale (CES-D)," Journal of psychosomatic research, vol. 46, no. 5, pp. 437-443, 1999.
[3] H. Nguyen and M.-L. Nguyen, "A deep neural architecture for sentence-level sentiment classification in twitter social networking," in International Conference of the Pacific Association for Computational Linguistics, 2017: Springer, pp. 15-27.
[4] A. Hu and S. Flaxman, "Multimodal sentiment analysis to explore the structure of emotions," in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018: ACM, pp. 350-358.
[5] D. E. Losada, F. Crestani, and J. Parapar, "CLEF 2017 eRisk Overview: Early Risk Prediction on the Internet: Experimental Foundations," in CLEF (Working Notes), 2017.
[6] F. Sadeque, D. Xu, and S. Bethard, "Measuring the latency of depression detection in social media," in Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 2018: ACM, pp. 495-503.
[7] K. Kroenke, R. L. Spitzer, and J. B. Williams, "The PHQ‐9: validity of a brief depression severity measure," Journal of general internal medicine, vol. 16, no. 9, pp. 606-613, 2001.
[8] S. Gallagher, "Time, emotion, and depression," Emotion Review, vol. 4, no. 2, pp. 127-132, 2012.
[9] M. De Choudhury, M. Gamon, S. Counts, and E. Horvitz, "Predicting depression via social media," in Seventh international AAAI conference on weblogs and social media, 2013.
[10] Y. Wang et al., "Understanding and discovering deliberate self-harm content in social media," in Proceedings of the 26th International Conference on World Wide Web, 2017: International World Wide Web Conferences Steering Committee, pp. 93-102.
[11] A. H. Orabi, P. Buddhitha, M. H. Orabi, and D. Inkpen, "Deep learning for depression detection of twitter users," in Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, 2018, pp. 88-97.
[12] G. Shen et al., "Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution," in IJCAI, 2017, pp. 3838-3844.
[13] T. Shen et al., "Cross-Domain Depression Detection via Harvesting Social Media," in IJCAI, 2018, pp. 1611-1617.
[14] X. Chen, M. D. Sykora, T. W. Jackson, and S. Elayan, "What about mood swings: identifying depression on twitter with temporal measures of emotions," in Companion of the The Web Conference 2018 on The Web Conference 2018, 2018: International World Wide Web Conferences Steering Committee, pp. 1653-1660.
[15] M. Y. Wu, C.-Y. Shen, E. T. Wang, and A. L. Chen, "A deep architecture for depression detection using posting, behavior, and living environment data," Journal of Intelligent Information Systems, pp. 1-20, 2018.
[16] X. Tian, P. Batterham, S. Song, X. Yao, and G. Yu, "Characterizing depression issues on sina weibo," International journal of environmental research and public health, vol. 15, no. 4, p. 764, 2018.
[17] A. G. Reece and C. M. Danforth, "Instagram photos reveal predictive markers of depression," EPJ Data Science, vol. 6, no. 1, p. 15, 2017.
[18] N. Andalibi, P. Ozturk, and A. Forte, "Depression-related imagery on instagram," in Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative work & social computing, 2015: ACM, pp. 231-234.
[19] Q. Cong, Z. Feng, F. Li, Y. Xiang, G. Rao, and C. Tao, "XA-BiLSTM: a Deep Learning Approach for Depression Detection in Imbalanced Data," in 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018: IEEE, pp. 1624-1627.
[20] K. Kang, C. Yoon, and E. Y. Kim, "Identifying depressive users in Twitter using multimodal analysis," in 2016 International Conference on Big Data and Smart Computing (BigComp), 2016: IEEE, pp. 231-238.
[21] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems, 2012, pp. 1097-1105.
[22] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
[23] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
[24] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in 2009 IEEE conference on computer vision and pattern recognition, 2009: Ieee, pp. 248-255.
[25] J. Silva, L. Coheur, A. C. Mendes, and A. Wichert, "From symbolic to sub-symbolic information in question classification," Artificial Intelligence Review, vol. 35, no. 2, pp. 137-154, 2011.
[26] S. Wang and C. D. Manning, "Baselines and bigrams: Simple, good sentiment and topic classification," in Proceedings of the 50th annual meeting of the association for computational linguistics: Short papers-volume 2, 2012: Association for Computational Linguistics, pp. 90-94.
[27] Y. Kim, "Convolutional neural networks for sentence classification," arXiv preprint arXiv:1408.5882, 2014.
[28] C. Dos Santos and M. Gatti, "Deep convolutional neural networks for sentiment analysis of short texts," in Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, 2014, pp. 69-78.
[29] X. Zhang and Y. LeCun, "Text understanding from scratch," arXiv preprint arXiv:1502.01710, 2015.
[30] A. Hassan and A. Mahmood, "Deep learning approach for sentiment analysis of short texts," in 2017 3rd international conference on control, automation and robotics (ICCAR), 2017: IEEE, pp. 705-710.
[31] D. Borth, R. Ji, T. Chen, T. Breuel, and S.-F. Chang, "Large-scale visual sentiment ontology and detectors using adjective noun pairs," in Proceedings of the 21st ACM international conference on Multimedia, 2013: ACM, pp. 223-232.
[32] Q. You, J. Luo, H. Jin, and J. Yang, "Robust image sentiment analysis using progressively trained and domain transferred deep networks," in Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.
[33] H.-R. Kim, Y.-S. Kim, S. J. Kim, and I.-K. Lee, "Building emotional machines: Recognizing image emotions through deep neural networks," IEEE Transactions on Multimedia, vol. 20, no. 11, pp. 2980-2992, 2018.
[34] T. Rao, M. Xu, and D. Xu, "Learning multi-level deep representations for image emotion classification," arXiv preprint arXiv:1611.07145, 2016.
[35] M. Oquab, L. Bottou, I. Laptev, and J. Sivic, "Learning and transferring mid-level image representations using convolutional neural networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 1717-1724.
[36] A. H. Yazdavar et al., "Semi-supervised approach to monitoring clinical depressive symptoms in social media," in Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, 2017: ACM, pp. 1191-1198.
[37] T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word representations in vector space," arXiv preprint arXiv:1301.3781, 2013.
[38] A. Paszke et al., "Automatic differentiation in pytorch," 2017.
[39] F. Pedregosa et al., "Scikit-learn: Machine learning in Python," Journal of machine learning research, vol. 12, no. Oct, pp. 2825-2830, 2011.
[40] M. Al-Mosaiwi and T. Johnstone, "In an absolute state: Elevated use of absolutist words is a marker specific to anxiety, depression, and suicidal ideation," Clinical Psychological Science, vol. 6, no. 4, pp. 529-542, 2018.
[41] Y.C. Huang, F.C. Chieh , A. L. Chen : "Predicting Depression Tendency based on Image, Text and Behavior Data from Instagram" in International Conference on Data Science, Technology and Applications, 2019