A Initial Experimental Evaluation of the NeuroMessenger: A Collaborative Tool to Improve the Empathy of Text Interactions

  • Breno Santana Santos
  • Methanias Colaço Júnior
  • Janisson Gois de Souza
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 738)


Empathy plays an important role in social interactions, such an effective teaching-learning process in a teacher-student relationship, and company-client or employee-customer relationship to retain potential clients and provide them with greater satisfaction. Increasingly, the Computer-Mediated Communication (CMC) support people in their interactions, especially when the interlocutors are geographically distant from one another. In CMC, there are different approaches to promote empathy in social or human-computer interactions. However, a little explored approach to gaining empathy in CMC is the use of the theory of Neurolinguistics that presents the possibility of developing a Preferred Representation System (PRS) for cognition in humans. This paper presents an initial experimental evaluation of the NeuroMessenger, a collaborative messenger library that uses the theory of Neurolinguistics to promote empathy by PRS identification and suggestion of textual matching based on the given PRS, using psychometry and text mining. The results showed that there was a difference between the means of grades in the empathy evaluation, in favor of NeuroMessenger. Although it is initial study, the results are encouraging, and more research on textual matching to gain empathy is needed.


Text mining Empathy Rapport Experimental evaluation Collaborative systems 


  1. 1.
    R. Ecker, Multiple-views analysis of computer-mediated discourses, in Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services, 2015, pp. 24:1–24:10Google Scholar
  2. 2.
    N. Liebman, D. Gergle, It’s (not) simply a matter of time: the relationship between CMC cues and interpersonal affinity, in Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, 2016, pp. 570–581Google Scholar
  3. 3.
    D.A. Morand, R.J. Ocker, Politeness theory and computer-mediated communication: a sociolinguistic approach to analyzing relational messages, in Proceedings of the 36th Annual Hawaii International Conference on System Sciences, 2003, pp. 1–10Google Scholar
  4. 4.
    R.T.Y. Hui, Coaching in computer-mediated communication at workplace, in 2015 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), 2015, pp. 215–219Google Scholar
  5. 5.
    R.M. Fuller, C.M. Vician, S.A. Brown, Longitudinal effects of computer-mediated communication anxiety on interaction in virtual teams. IEEE Trans. Prof. Commun. 59(3), 166–185 (2016)CrossRefGoogle Scholar
  6. 6.
    R. Heckman, et al., Emergent decision-making practices in technology-supported self-organizing distributed teams, in ICIS 2006—International Conference on Information Systems, 2006, pp. 10–13Google Scholar
  7. 7.
    M. Pimentel, H. Fuks, Sistemas Colaborativos (Elsevier, Rio de Janeiro, 2012)Google Scholar
  8. 8.
    N.J.B. Jamil, Z. Tasir, Students’ social presence in online learning system, in 2014 International Conference on Teaching and Learning in Computing and Engineering, 2014, pp. 289–292Google Scholar
  9. 9.
    J. Khalfallah, J.B.H. Slama, Facial expression recognition for intelligent tutoring Systems in Remote Laboratories Platform. Procedia Comput. Sci. 73, 274–281 (2015)CrossRefGoogle Scholar
  10. 10.
    M. Martinez, High attrition rates in e-learning: challenges, predictors, and solutions. E-Learning Dev. J. 14(1) (2003)Google Scholar
  11. 11.
    L. Rourke, T. Anderson, D.R. Garrison, W. Archer, Assessing social presence in asynchronous text-based computer conferencing. Int. J. E-Learning Distance Educ. 14(2), 50–71 (2007)Google Scholar
  12. 12.
    M. Siitonen, M. Olbertz-Siitonen, I Am Right Here With You: constructing presence in distributed teams, in Proceedings of International Conference on Making Sense of Converging Media, 2013, pp. 11:11–11:16Google Scholar
  13. 13.
    A. Khawaji, F. Chen, N. Marcus, J. Zhou, Trust and cooperation in text-based computer-mediated communication, in Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration, 2013, pp. 37–40Google Scholar
  14. 14.
    Y.R. Tausczik, J.W. Pennebaker, Improving teamwork using real-time language feedback, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2013, pp. 459–468Google Scholar
  15. 15.
    B.S. Santos, M.C. Júnior, M.A.S.N. Nunes, Approaches for generating empathy: a systematic mapping, in Information Technology—New Generations: 14th International Conference on Information Technology, ed. By S. Latifi (Springer, Cham, 2018), pp. 715–722Google Scholar
  16. 16.
    R. Bandler, R. Dilts, J. DeLozier, J. Grinder, Neuro-Linguistic Programming: The Study of the Structure of Subjective Experience, 1st edn. (Meta, Capitola, 1980)Google Scholar
  17. 17.
    R. Ready, K. Burton, Programação Neurolinguística para Leigos (Alta Books, Rio de Janeiro, 2014)Google Scholar
  18. 18.
    J. Sturt et al., Neurolinguistic programming: a systematic review of the effects on health outcomes. Br. J. Gen. Pract. 62(604), 757–764 (2012)CrossRefGoogle Scholar
  19. 19.
    K.A. Dent, Cognitive Styles: Essence and Origins: Herman A. Witkin and Donald R. Goodenough, International Universities Press, New York, 1983. J. Am. Acad. Psychoanal. Dyn. Psychiatry 11(4), 635–636 (1983)Google Scholar
  20. 20.
    M. Koć-Januchta, T. Höffler, G.-B. Thoma, H. Prechtl, D. Leutner, Visualizers versus verbalizers: Effects of cognitive style on learning with texts and pictures—an eye-tracking study. Comput. Human Behav. 68, 170–179 (2017)CrossRefGoogle Scholar
  21. 21.
    J. Simuth, I. Sarmany-Schuller, The preferences of cognitive style among university students from various study fields. Procedia 191, 2537–2540 (2015) Google Scholar
  22. 22.
    R.R.B. Giaxa, Tipos Psicológicos e Empatia: Contribuições da Psicologia para a Formação do (ser) Médico (Universidade Autónoma de Lisboa, 2015)Google Scholar
  23. 23.
    J. Oxley, The Moral Dimensions of Empathy (Palgrave Macmillan, New York, 2011)CrossRefGoogle Scholar
  24. 24.
    M.C. Rodrigues, N.N. Ribeiro, Avaliação da empatia em crianças participantes e não participantes de um programa de desenvolvimento sociocognitivo. Psicol. Teor. e prática 13(2), 114–126 (2011)Google Scholar
  25. 25.
    A. Robbins, Poder Sem Limites: o caminho do sucesso pessoal pela programação neurolinguística (BestSeller, Rio de Janeiro, 2016)Google Scholar
  26. 26.
    M. Hojat, Empathy in Patient Care: Antecedents, Development, Measurement, and Outcomes (Springer Science & Business Media, Philadelphia, 2007)Google Scholar
  27. 27.
    A.R.M.S. Palhoco, Estudo da empatia e da percepção de emoções em psicoterapeutas e estudantes de psicologia (Universidade de Lisboa, 2011)Google Scholar
  28. 28.
    D. Goleman, Inteligência emocional: a teoria revolucionária que redefine o que é ser inteligente (Objetiva, Rio de Janeiro, 2012)Google Scholar
  29. 29.
    M. Colaço Júnior, Identificação e Validação do Perfil Neurolinguístico de Programadores através da Mineração de Repositórios de Engenharia de Software (Universidade Federal da Bahia, 2011)Google Scholar
  30. 30.
    M.C. Júnior, M. de Fátima Menezes, D. Corumba, M. Mendonça, B.S. Santos, Do software engineers have preferred representational systems? J. Res. Pract. Inf. Technol. 47(1), 23 (2015)Google Scholar
  31. 31.
    M. Colaço Júnior, M. Mendonça, M.A.D.F. Farias, P. Henrique, D. Corumba, A neurolinguistic-based methodology for identifying OSS developers context-specific preferred representational systems, in ICSEA 2012: The Seventh International Conference on Software Engineering Advances, 2012, pp. 112–121Google Scholar
  32. 32.
    M. Colaço Júnior, M.A.D.F. Farias, I. Maciel, P.H. Dos Santos, M. Mendonca, Triangulating experiments in an industrial setting to evaluate preferred representational systems of software developers, in 2014 Brazilian Symposium on Software Engineering, 2014, pp. 71–80Google Scholar
  33. 33.
    T.B. Kashdan et al., More than words: contemplating death enhances positive emotional word use. Pers. Individ. Dif. 71, 171–175 (2014) CrossRefGoogle Scholar
  34. 34.
    A.M. Katz, S.J. Czech, S.M. Orsillo, Putting values into words: an examination of the text characteristics of values articulation. J. Context. Behav. Sci. 3(1), 16–20 (2014)CrossRefGoogle Scholar
  35. 35.
    C. Wohlin, P. Runeson, M. Höst, M.C. Ohlsson, B. Regnell, A. Wesslen, Experimentation in Software Engineering (Springer, New York, 2012)CrossRefGoogle Scholar
  36. 36.
    V.R. Basili, D.M. Weiss, A methodology for collecting valid software engineering data. IEEE Trans. Softw. Eng. 10(6), 728–738 (1984)CrossRefGoogle Scholar
  37. 37.
    R. Van Solingen, E. Berghout, The Goal/Question/Metric Method: A Practical Guide for Quality Improvement of Software Development (McGraw-Hill, London, 1999)Google Scholar
  38. 38.
    N. de Tec. da Inf. da UFS, SIGAA—Sistema Integrado de Gestão de Atividades Acadêmicas [Online], Accessed 01 Oct 2017
  39. 39.
    J.N. Bailenson, N. Yee, Digital chameleons automatic assimilation of nonverbal gestures in immersive virtual environments. Psychol. Sci. 16(10), 814–819 (2005)CrossRefGoogle Scholar
  40. 40.
    B.S. Hasler, G. Hirschberger, T. Shani-Sherman, D.A. Friedman, Virtual peacemakers: mimicry increases empathy in simulated contact with virtual outgroup members. Cyberpsychol. Behav. Soc. Netw. 17(12), 766–771 (2014)CrossRefGoogle Scholar
  41. 41.
    N. Kummer, D. Kadish, A. Dulic, and H. Najjaran, The empathy machine, in Conference Proceedings—IEEE International Conference on Systems, Man and Cybernetics, 2012, pp. 2265–2271Google Scholar
  42. 42.
    C.S.S. Tan, K. Luyten, J. Van Den Bergh, J. Schöning, K. Coninx, The role of physiological cues during remote collaboration. Presence 23(1), 90–107 (2014)CrossRefGoogle Scholar
  43. 43.
    H.B.M.S. Paro, R.M. Daud-Gallotti, I.C. Tibério, R.M.C. Pinto, M.A. Martins, Brazilian version of the Jefferson Scale of Empathy: psychometric properties and factor analysis. BMC Med. Educ. 12(1), 73 (2012)CrossRefGoogle Scholar
  44. 44.
    IBM, IBM SPSS Software (2012)Google Scholar
  45. 45.
    M. Bramer, Principles of Data Mining, 3rd edn. (Springer, London, 2016)CrossRefGoogle Scholar
  46. 46.
    T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. (Springer, New York, 2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Breno Santana Santos
    • 1
  • Methanias Colaço Júnior
    • 1
  • Janisson Gois de Souza
    • 2
  1. 1.Postgraduate Program in Computer Science (PROCC)Federal University of Sergipe (UFS)São CristóvãoBrazil
  2. 2.Competitive Intelligence Research and Practice Group (NUPIC)Federal University of Sergipe (UFS)ItabaianaBrazil

Personalised recommendations