Skip to main content

Interactive Analysis of the Discussion from a Virtual Community on Neuroscience

  • 393 Accesses

Part of the Communications in Computer and Information Science book series (CCIS,volume 1068)

Abstract

Social media on the Internet has been promoting disruptive transformations in the society, enabling new possibilities for knowledge construction. Nowadays, researchers can fastly gather a large number of discussions from a virtual community of interest for analysis. This paper presents the study of a relevant community on neuroscience, with more than 43,000 registered members. The research method employs a process based on Grounded Theory and Knowledge Discovery in Databases (KDD), using a tool crafted to support interactively and iteratively use of data mining algorithms such as topic modeling and sentiment analysis. From the analysis of 2,927 posts and 19,227 comments, the results reveal the most prominent subject regards Alzheimer’s disease, followed by general acknowledgments and requests for help whether concerning symptom assessment, examination results analysis, and medical advice. Most of the identified topics have a positive polarity, indicating that interactions are predominantly friendly. Negative feelings emerged from controversial topics, being mostly non-technical or speculative subjects such as mind control techniques, help with MRI results, answers to medical advice, and theories about consciousness. The findings reinforce the feasibility of such studies and show useful insights regarding the community interest in neuroscience.

Keywords

  • Neuroscience
  • Virtual community
  • Analysis

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-36636-0_5
  • Chapter length: 20 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   64.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-36636-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   84.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.

References

  1. Abdellaoui, R., Foulquié, P., Texier, N., Faviez, C., Burgun, A., Schück, S.: Detection of cases of noncompliance to drug treatment in patient forum posts: topic model approach. J. Med. Internet Res. 20(3), e85 (2018)

    CrossRef  Google Scholar 

  2. Carvalho, D., Marcacini, R., Lucena, C., Rezende, S.: A process to support analysts in exploring and selecting content from online forums. Soc. Netw. 3(02), 86 (2014)

    CrossRef  Google Scholar 

  3. Chen, A.T., Zhu, S.H., Conway, M.: What online communities can tell us about electronic cigarettes and hookah use: a study using text mining and visualization techniques. J. Med. Internet Res. 17(9), e220 (2015)

    CrossRef  Google Scholar 

  4. Cho, H., Silver, N., Na, K., Adams, D., Luong, K.T., Song, C.: Visual cancer communication on social media: an examination of content and effects of# melanomasucks. J. Med. Internet Res. 20(9), e10501 (2018)

    CrossRef  Google Scholar 

  5. Choo, J., Lee, C., Reddy, C.K., Park, H.: UTOPIAN: user-driven topic modeling based on interactive nonnegative matrix factorization. IEEE Trans. Vis. Comput. Graphics 19(12), 1992–2001 (2013)

    CrossRef  Google Scholar 

  6. Clark, T., Kinoshita, J.: Alzforum and SWAN: the present and future of scientific web communities. Briefings Bioinf. 8(3), 163–171 (2007)

    CrossRef  Google Scholar 

  7. Das, S., et al.: Pain research forum: application of scientific social media frameworks in neuroscience. Front. Neuroinf. 8, 21 (2014)

    CrossRef  Google Scholar 

  8. De Choudhury, M., De, S.: Mental health discourse on reddit: self-disclosure, social support, and anonymity. In: Eighth International AAAI Conference on Weblogs and Social Media (2014)

    Google Scholar 

  9. Debuse, J., de la Iglesia, B., Howard, C., Rayward-Smith, V.: Building the KDD roadmap. In: Roy, R. (eds.) Industrial Knowledge Management, pp. 179–196. Springer, London (2001). https://doi.org/10.1007/978-1-4471-0351-6_12

    CrossRef  Google Scholar 

  10. Fan, W., Gordon, M.D.: The power of social media analytics. Commun. ACM 57(6), 74–81 (2014)

    CrossRef  Google Scholar 

  11. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The KDD process for extracting useful knowledge from volumes of data. Commun. ACM 39(11), 27–34 (1996)

    CrossRef  Google Scholar 

  12. Glaser, B.G., Strauss, A.L.: Discovery of Grounded Theory: Strategies for Qualitative Research. Routledge (2017)

    Google Scholar 

  13. Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier (2011)

    Google Scholar 

  14. Hu, Y., Boyd-Graber, J., Satinoff, B., Smith, A.: Interact. Top. Model. Mach. Learn. 95(3), 423–469 (2014)

    MathSciNet  CrossRef  Google Scholar 

  15. Hutto, C.J., Gilbert, E.: VADER: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media (2014)

    Google Scholar 

  16. Kim, S.J., Marsch, L.A., Hancock, J.T., Das, A.K.: Scaling up research on drug abuse and addiction through social media big data. J. Med. Internet Res. 19(10), e353 (2017)

    CrossRef  Google Scholar 

  17. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788 (1999)

    CrossRef  Google Scholar 

  18. Liu, B.: Sentiment analysis and opinion mining. Synth. lect. Hum. Lang. Technol. 5(1), 1–167 (2012)

    CrossRef  Google Scholar 

  19. Matthews, K.A., et al.: Racial and ethnic estimates of alzheimer’s disease and related dementias in the united states (2015–2060) in adults aged\(\geqslant \) 65 years. Alzheimer’s Dement. 15(1), 17–24 (2019)

    CrossRef  Google Scholar 

  20. Meshi, D., Tamir, D.I., Heekeren, H.R.: The emerging neuroscience of social media. Trends Cogn. Sci. 19(12), 771–782 (2015)

    CrossRef  Google Scholar 

  21. Muller, M., Guha, S., Baumer, E.P., Mimno, D., Shami, N.S.: Machine learning and grounded theory method: convergence, divergence, and combination. In: Proceedings of the 19th International Conference on Supporting Group Work, pp. 3–8. ACM (2016)

    Google Scholar 

  22. O’callaghan, D., Greene, D., Carthy, J., Cunningham, P.: An analysis of the coherence of descriptors in topic modeling. Expert Syst. Appl. 42(13), 5645–5657 (2015)

    CrossRef  Google Scholar 

  23. Qiu, B., et al.: Get online support, feel better-sentiment analysis and dynamics in an online cancer survivor community. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, pp. 274–281. IEEE (2011)

    Google Scholar 

  24. Shneiderman, B., Preece, J., Pirolli, P.: Realizing the value of social media requires innovative computing research. Commun. ACM 54(9), 34–37 (2011)

    CrossRef  Google Scholar 

  25. Song, Y., Pan, S., Liu, S., Zhou, M.X., Qian, W.: Topic and keyword re-ranking for LDA-based topic modeling. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1757–1760. ACM (2009)

    Google Scholar 

  26. Urquhart, C., Fernández, W.: Using grounded theory method in information systems: the researcher as blank slate and other myths. In: Willcocks, L.P., Sauer, C., Lacity, M.C. (eds.) Enacting Research Methods in Information Systems: Volume 1, pp. 129–156. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29266-3_7

    CrossRef  Google Scholar 

  27. Vasconcellos-Silva, P.R., Carvalho, D., Lucena, C.: Word frequency and content analysis approach to identify demand patterns in a virtual community of carriers of hepatitis C. Interact. J. Med. Res. 2(2), e12 (2013)

    CrossRef  Google Scholar 

  28. Weninger, T., Zhu, X.A., Han, J.: An exploration of discussion threads in social news sites: a case study of the reddit community. In: 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), pp. 579–583. IEEE (2013)

    Google Scholar 

  29. Zheng, K., Li, A., Farzan, R.: Exploration of online health support groups through the lens of sentiment analysis. In: Chowdhury, G., McLeod, J., Gillet, V., Willett, P. (eds.) iConference 2018. LNCS, vol. 10766, pp. 145–151. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78105-1_19

    CrossRef  Google Scholar 

  30. Zou, C., Hou, D.: LDA analyzer: a tool for exploring topic models. In: 2014 IEEE International Conference on Software Maintenance and Evolution, pp. 593–596. IEEE (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rafael José de Alencar Almeida .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

de Alencar Almeida, R.J., Carvalho, D.B.F. (2019). Interactive Analysis of the Discussion from a Virtual Community on Neuroscience. In: Cota, V., Barone, D., Dias, D., Damázio, L. (eds) Computational Neuroscience. LAWCN 2019. Communications in Computer and Information Science, vol 1068. Springer, Cham. https://doi.org/10.1007/978-3-030-36636-0_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-36636-0_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36635-3

  • Online ISBN: 978-3-030-36636-0

  • eBook Packages: Computer ScienceComputer Science (R0)