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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
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)
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)
Clark, T., Kinoshita, J.: Alzforum and SWAN: the present and future of scientific web communities. Briefings Bioinf. 8(3), 163–171 (2007)
Das, S., et al.: Pain research forum: application of scientific social media frameworks in neuroscience. Front. Neuroinf. 8, 21 (2014)
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)
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
Fan, W., Gordon, M.D.: The power of social media analytics. Commun. ACM 57(6), 74–81 (2014)
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)
Glaser, B.G., Strauss, A.L.: Discovery of Grounded Theory: Strategies for Qualitative Research. Routledge (2017)
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier (2011)
Hu, Y., Boyd-Graber, J., Satinoff, B., Smith, A.: Interact. Top. Model. Mach. Learn. 95(3), 423–469 (2014)
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)
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)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788 (1999)
Liu, B.: Sentiment analysis and opinion mining. Synth. lect. Hum. Lang. Technol. 5(1), 1–167 (2012)
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)
Meshi, D., Tamir, D.I., Heekeren, H.R.: The emerging neuroscience of social media. Trends Cogn. Sci. 19(12), 771–782 (2015)
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)
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)
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)
Shneiderman, B., Preece, J., Pirolli, P.: Realizing the value of social media requires innovative computing research. Commun. ACM 54(9), 34–37 (2011)
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)
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
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)
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)
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
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
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)