Early Risk Detection of Anorexia on Social Media

  • Diana Ramírez-Cifuentes
  • Marc Mayans
  • Ana FreireEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11193)


This paper proposes an approach for the early detection of anorexia nervosa (AN) on social media. We present a machine learning approach that processes the texts written by social media users. This method relies on a set of features based on domain-specific vocabulary, topics, psychological processes, and linguistic information extracted from the users’ writings. This approach penalizes the delay in detecting positive cases in order to classify the users in risk as early as possible. Identifying anorexia early, along with an appropriate treatment, improves the speed of recovery and the likelihood of staying free of the illness. The results of this work showed that our proposal is suitable for the early detection of AN symptoms.


Early risk detection Eating disorders Social media Anorexia Machine learning 



This work was supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).


  1. 1.
    Agresti, A.: Categorical Data Analysis. Wiley Series in Probability and Statistics. Wiley, Hoboken (2013). Scholar
  2. 2.
    Arseniev-Koehler, A., Lee, H., McCormick, T., Moreno, M.: #proana: pro-eating disorder socialization on twitter, vol. 58, April 2016CrossRefGoogle Scholar
  3. 3.
    American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders: DSM-5, 5th edn. American Psychiatric Association, Arlington (2013)Google Scholar
  4. 4.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003). Scholar
  5. 5.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). Scholar
  6. 6.
    Campbell, K., Peebles, R.: Eating disorders in children and adolescents: state of the art review. Scholar
  7. 7.
    Chancellor, S., Lin, Z., Goodman, E.L., Zerwas, S., De Choudhury, M.: Quantifying and predicting mental illness severity in online pro-eating disorder communities. In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, CSCW 2016, pp. 1171–1184. ACM, New York (2016).
  8. 8.
    Coalition, E.D.: Facts about eating disorders: what the research shows (2016)Google Scholar
  9. 9.
    Coppersmith, G., Harman, C., Dredze, M.: Measuring post traumatic stress disorder in Twitter, pp. 579–582, January 2014Google Scholar
  10. 10.
    Coppersmith, G., Dredze, M., Harman, C.: Quantifying mental health signals in Twitter (2014)Google Scholar
  11. 11.
    Coppersmith, G., Dredze, M., Harman, C., Hollingshead, K., Mitchell, M.: CLPsych 2015 shared task: depression and PTSD on twitter. In: CLPsych@HLT-NAACL (2015)Google Scholar
  12. 12.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). Scholar
  13. 13.
    De Choudhury, M., Counts, S., Horvitz, E.J., Hoff, A.: Characterizing and predicting postpartum depression from shared facebook data. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, CSCW 2014, pp. 626–638. ACM, New York (2014).
  14. 14.
    De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: AAAI, July 2013.
  15. 15.
    Elberrichi, Z.: Text mining using n-grams, January 2006Google Scholar
  16. 16.
    Guntuku, S.C., Yaden, D.B., Kern, M.L., Ungar, L.H., Eichstaedt, J.C.: Detecting depression and mental illness on social media: an integrative review. Curr. Opin. Behav. Sci. 18, 43–49 (2017). Big data in the behavioural sciencesCrossRefGoogle Scholar
  17. 17.
    Keski-Rahkonen, A., Mustelin, L.: Epidemiology of eating disorders in Europe: prevalence, incidence, comorbidity, course, consequences, and risk factors, vol. 29, September 2016Google Scholar
  18. 18.
    Leiva, Victor, Freire, Ana: Towards suicide prevention: early detection of depression on social media. In: Kompatsiaris, Ioannis, et al. (eds.) INSCI 2017. LNCS, vol. 10673, pp. 428–436. Springer, Cham (2017). Scholar
  19. 19.
    Losada, David E., Crestani, Fabio: A test collection for research on depression and language use. In: Fuhr, Norbert, et al. (eds.) CLEF 2016. LNCS, vol. 9822, pp. 28–39. Springer, Cham (2016). Scholar
  20. 20.
    Maximilian, F.M., Norbert, Q.: Mortality in eating disorders - results of a large prospective clinical longitudinal study. Int. J. Eat. Disord. 49(4), pp. 391–401. Scholar
  21. 21.
    Park, M., Cha, C., Cha, M.: Depressive moods of users portrayed in Twitter, pp. 1–8, January 2012Google Scholar
  22. 22.
    Pennebaker, J.W., Chung, C.K., Ireland, M., Gonzales, A., Booth, R.J.: The Development and Psychometric Properties of LIWC2007. This article is published by LIWC Inc., Austin, Texas 78703 USA in conjunction with the LIWC2007 software program.
  23. 23.
    PreoŢiuc-Pietro, D., et al.: The role of personality, age, and gender in tweeting about mental illness. In: Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pp. 21–30. Association for Computational Linguistics (2015).
  24. 24.
    Prieto, V.M., Matos, S., Alvarez, M., Cacheda, F., Oliveira, J.L.: Twitter: a good place to detect health conditions. PloS one 9, e86191 (2014)CrossRefGoogle Scholar
  25. 25.
    Ramchoun, H., Amine, M., Idrissi, J., Ghanou, Y., Ettaouil, M.: Multilayer perceptron: architecture optimization and training. IJIMAI 4(1), 26–30 (2016)CrossRefGoogle Scholar
  26. 26.
    Reece, A.G., Reagan, A.J., Lix, K.L.M., Dodds, P.S., Danforth, C.M., Langer, E.J.: Forecasting the onset and course of mental illness with twitter data. Sci. Rep. 7, 13006 (2017)CrossRefGoogle Scholar
  27. 27.
    Resnik, P., Garron, A., Resnik, R.: Using topic modeling to improve prediction of neuroticism and depression in college students, pp. 1348–1353, January 2013Google Scholar
  28. 28.
    Schwartz, H.A., et al.: Towards assessing changes in degree of depression through Facebook (2014)Google Scholar
  29. 29.
    Treasure, J., Russell, G.: The case for early intervention in anorexia nervosa: theoretical exploration of maintaining factors. Br. J. Psychiatry 199(1), 5–7 (2011). Scholar
  30. 30.
    Tsugawa, S., Kikuchi, Y., Kishino, F., Nakajima, K., Itoh, Y., Ohsaki, H.: Recognizing depression from twitter activity. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015, pp. 3187–3196. ACM, New York (2015).
  31. 31.
    Wang, T., Brede, M., Ianni, A., Mentzakis, E.: Detecting and characterizing eating-disorder communities on social media. In: WSDM (2017)Google Scholar
  32. 32.
    Wijbrand, H.H., van Hoeken, D.: Review of the prevalence and incidence of eating disorders. Int. J. Eat. Disord. 34(4), 383–396. Scholar
  33. 33.
    Wilson, J.L., Peebles, R., Hardy, K.K., Litt, I.F.: Surfing for thinness: a pilot study of pro-eating disorder web site usage in adolescents with eating disorders. Pediatrics 118(6), e1635-43 (2006)CrossRefGoogle Scholar
  34. 34.
    Zhang, L., Huang, X., Liu, T., Chen, Z., Zhu, T.: Using linguistic features to estimate suicide probability of Chinese microblog users. CoRR abs/1411.0861 (2014).

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Diana Ramírez-Cifuentes
    • 1
  • Marc Mayans
    • 1
  • Ana Freire
    • 1
    Email author
  1. 1.Web Science and Social Computing Research GroupUniversitat Pompeu Fabra, BarcelonaBarcelonaSpain

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