Predicting Multiple Risky Behaviors via Multimedia Content

  • Yiheng ZhouEmail author
  • Jingyao Zhan
  • Jiebo Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10540)


Risky behaviors pose a growing threat to our society. In the case of drug consumption, according to National Survey on Drug Use and Health, “substance abuse costs our society more than 484 billion dollars a year”, which is about thrice of what we spend on cancer. Researchers have started studying risky behaviors through big data from social media. However, to our best knowledge, most of the existing schemes focus on only one risky behavior, despite that the research in public health and psychology has shown us that there exist correlations among risky behaviors. In this work, in order to exploit such correlation, we select five risky behaviors, namely drug consumption, drinking, sleep disorder, depression, and eating disorder. Furthermore, we propose a deep learning neural network constructed by combining recurrent neural networks (RNN) and convolutional neural networks (CNN) to effectively predict whether an Instagram user will conduct which kind of risky behaviors in the near future.


Multimedia Social media Risky behaviors Multi-task learning Prediction Health informatics 


  1. 1.
    National Survey on Drug Use and Health (2014)Google Scholar
  2. 2.
    Zhou, Y., Sani, N., Luo, J.: Understanding Illicit Drug Use Behaviors by Mining Social Media. SBP-BRiMS, Washington DC (2016)Google Scholar
  3. 3.
    National Institute on Drug Abuse. NIDA (2015)Google Scholar
  4. 4.
    Caruana, R.: Multi-Task Learning. Machine Learning (1997)Google Scholar
  5. 5.
    Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting Depression via Social Media (2013)Google Scholar
  6. 6.
    Buntain, C., Golbeck, J.: This is your Twitter on drugs. any questions? In: Proceedings of the 24th International Conference on World Wide Web (WWW) (2015)Google Scholar
  7. 7.
    Pang, R., Baretto, A., Kautz, H., Luo, J.: Monitoring adolescent alcohol use via multimodal analysis in social multimedia. In: IEEE Big Data Conference (2015)Google Scholar
  8. 8.
    Menon, A., Farmer, F., Whalen, T., Hua, B., Najib, K., Gerber, M.: Automatic identification of alcohol-related promotions on Twitter and prediction of promotion spread. In: Systems and Information Engineering Design Symposium (SIEDS) (2014)Google Scholar
  9. 9.
    Germain, S., Hooley, J.: Direct and Indirect Forms of Non-suicidal Self-injury: Evidence for a Distinction. Psychiatry Research (2011)Google Scholar
  10. 10.
    Jaques, N., Taylor, S., Nosakhare, E., Sano, A., Picard, R.: NIPS 2016 Workshop on Machine Learning for Health (2016)Google Scholar
  11. 11.
    Mike, B.: Instagram statistics (2014)Google Scholar
  12. 12.
    Face API, version 1.0, Project Oxford, MicrosoftGoogle Scholar
  13. 13.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR) (2015)Google Scholar
  14. 14.
    Russakovsky*, O., Deng*, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: (* = equal contribution) ImageNet Large Scale Visual Recognition Challenge. In: IJCV (2015)Google Scholar
  15. 15.
    Zhou, Y., Glenn, C., Luo, J.: Understanding and predicting multiple risky behaviors from social media. In: AAAI 2017 Joint Workshop on Health Intelligence, San Francisco, CA, February 2017Google Scholar
  16. 16.
    Hsieh, C., Chang, K., Lin, C., Keerthi, S., Sundararajan, S.: A dual coordinate descent method for large-scale linear svM. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 408–415. ACM, New York (2008). doi: 10.1145/1390156.1390208. ISBN 978-1-60558-205-4
  17. 17.
    Hutto, C.J., Gilbert, E.: VADER: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International Conference on Weblogs and Social Media (ICWSM-14) (2014)Google Scholar
  18. 18.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems (NIPS) (2013)Google Scholar
  19. 19.
    Agrawal, R.; Srikant, R; 1994. Fast algorithms for mining association rules in large databases. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pages 487–499, Santiago, Chile, September 1994Google Scholar
  20. 20.
    Harp, A., Irving, G., et al.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2015)Google Scholar
  21. 21.
    Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularizations. In: ICLR (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of RochesterRochesterUSA

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