Advertisement

Estimating Support Scores of Autism Communities in Large-Scale Web Information Systems

  • Nguyen ThinEmail author
  • Nguyen Hung
  • Svetha Venkatesh
  • Dinh Phung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10569)

Abstract

Individuals with Autism Spectrum Disorder (ASD) have been shown to prefer communication at a socio-spatial distance. So while rarely found in the real world, autism communities are popular in Web-based forums, convenient for people with ASD to seek and share health related information. Reddit is one such avenue for people of common interest to connect, forming communities of specific interest, namely subreddits. This work aims to estimate support scores provided by a popular subreddit interested in ASD – www.reddit.com/r/aspergers. The scores were measured in both the quantities and qualities of the conversations in the forum, including conversational involvement, emotional, and informational support. The support scores of the subreddit Aspergers was compared with that of an average subreddit derived from entire Reddit, represented by two big corpora of approximately 200 million Reddit posts and 1.66 billion Reddit comments. The ASD subreddit was found to be a supportive community, having far higher support scores than did the average subreddit. Apache Spark, an advanced cluster computing framework, is employed to speed up processing of the large corpora. Scalable machine learning techniques implemented in Spark help discriminate the content made in Aspergers versus other subreddits and automatically discover linguistic predictors of ASD within minutes, providing timely reports.

Keywords

Big data Apache Spark Large-scale distributed computing Support scores Autism communities 

Notes

Acknowledgment

This work is partially supported by the Telstra-Deakin Centre of Excellence in Big Data and Machine Learning.

References

  1. 1.
    American Psychiatric Association.: Diagnostic and Statistical Manual of Mental Disorders, 5th edn. American Psychiatric Publishing, Arlington (2013)Google Scholar
  2. 2.
    Bradley, M.M., Lang, P.J.: Affective norms for English words (ANEW): instruction manual and affective ratings (1999)Google Scholar
  3. 3.
    De Choudhury, M., De, S.: Mental health discourse on Reddit: self-disclosure, social support, and anonymity. In: Proceedings of the International AAAI Conference on Weblogs and Social Media, pp. 71–80 (2014)Google Scholar
  4. 4.
    Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., Freeman, J., Tsai, D.B., Amde, M., Owen, S.: MLlib: machine learning in Apache Spark. J. Mach. Learn. Res. 17(34), 1–7 (2016)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Nguyen, T.: Mood patterns and affective lexicon access in weblogs. In: Proceedings of the ACL Student Research Workshop, pp. 43–48 (2010)Google Scholar
  6. 6.
    Nguyen, V., Nguyen, D.T., Le, T., Venkatesh, S., Phung, D.: One-pass logistic regression for label-drift and large-scale classification on distributed systems. In: Proceedings of International Conference on Data Mining (ICDM), pp. 1113–1118 (2016)Google Scholar
  7. 7.
    Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic inquiry and word count (LIWC) [Computer software]. LIWC Inc (2007)Google Scholar
  8. 8.
    Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)CrossRefGoogle Scholar
  9. 9.
    Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the USENIX Conference on Hot Topics in Cloud Computing, p. 10 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nguyen Thin
    • 1
    Email author
  • Nguyen Hung
    • 1
  • Svetha Venkatesh
    • 1
  • Dinh Phung
    • 1
  1. 1.Centre for Pattern Recognition and Data AnalyticsDeakin UniversityGeelongAustralia

Personalised recommendations