Skip to main content
SpringerLink
Log in

How Can Natural Language Processing and Generative AI Address Grand Challenges of Quantitative User Personas?

  • Conference paper
  • First Online:
HCI International 2023 – Late Breaking Papers (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14059))

Included in the following conference series:

  • 816 Accesses

Abstract

Human-computer interaction (HCI) and natural language processing (NLP) can engage in mutually beneficial collaboration. This article summarizes previous literature to identify grand challenges for the application of NLP in quantitative user personas (QUPs), which exemplifies such collaboration. Grand challenges provide a collaborative starting point for researchers working at the intersection of NLP and QUPs, towards improved user experiences. NLP research could also benefit from focusing on generating user personas by introducing new solutions to specific NLP tasks, such as classification and generation. We also discuss the novel opportunities introduced by Generative AI to address the grand challenges, offering illustrative examples.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://chat.openai.com/share/52da0742-1a00-48c7-988f-1c954570e148.

  2. 2.

    ibid.

References

  1. Ali Amer Jid Almahri, F., Bell, D., Arzoky, M.: Personas design for conversational systems in education. Informatics 6(4), 46 (2019). https://doi.org/10.3390/informatics6040046

  2. Amin, M.M., Cambria, E., Schuller, B.W.: Will affective computing emerge from foundation models and general artificial intelligence? a first evaluation of ChatGPT. IEEE Intell. Syst. 38(2), 15–23 (2023)

    Article  Google Scholar 

  3. An, J., Kwak, H., Jung, S., Salminen, J., Jansen, B.J.: Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data. Soc. Netw. Anal. Min. 8(1), 1–19 (2018). https://doi.org/10.1007/s13278-018-0531-0

    Article  Google Scholar 

  4. An, J., Kwak, H., Salminen, J., Jung, S.G., Jansen, B.J.: Imaginary people representing real numbers: generating personas from online social media data. ACM Trans. Web (TWEB) 12(3), 1–26 (2018)

    Google Scholar 

  5. Bamman, D., O’Connor, B., Smith, N.A.: Learning latent personas of film characters, p. 10. Bulgaria, Sofia (2013)

    Google Scholar 

  6. Bødker, S., Christiansen, E., Nyvang, T., Zander, P.O.: Personas, people and participation: challenges from the trenches of local government. In: The 12th Participatory Design Conference, p. 91. ACM Press, Roskilde (2012). https://doi.org/10.1145/2347635.2347649. http://dl.acm.org/citation.cfm?doid=2347635.2347649. Accessed 31 Mar 2020

  7. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)

    MATH  Google Scholar 

  8. Brosas, H., Lim, E., Sevilla, D., Silva, D., Ong, E.: Classifying and extracting data from facebook posts for online persona identification. In: Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation (2018)

    Google Scholar 

  9. Burns, E., Laskowski, N., Tucci, L.: What is artificial intelligence (ai)? definition, benefits and use cases (2022). https://www.techtarget.com/searchenterpriseai/definition/AI-Artificial-Intelligence. Accessed 01 Sept 2022

  10. Cambria, E.: Affective computing and sentiment analysis. IEEE Intell. Syst. 31(2), 102–107 (2016)

    Article  Google Scholar 

  11. Cambria, E., Liu, Q., Decherchi, S., Xing, F., Kwok, K.: Senticnet 7: a commonsense-based neurosymbolic ai framework for explainable sentiment analysis. In: Proceedings of LREC 2022 (2022)

    Google Scholar 

  12. Candello, H., et al.: Cui@chi: mapping grand challenges for the conversational user interface community. In: CHI 2020: CHI Conference on Human Factors in Computing Systems, pp. 1–8. ACM, Honolulu (2020). https://doi.org/10.1145/3334480.3375152. https://dl.acm.org/doi/10.1145/3334480.3375152. Accessed 09 June 2022

  13. Chapman, C., Milham, R.P.: The personas’ new clothes: methodological and practical arguments against a popular method. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 50, no. 5, pp. 634–636 (2006). https://doi.org/10.1177/154193120605000503

  14. Chu, E., Vijayaraghavan, P., Roy, D.: Learning personas from dialogue with attentive memory networks. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2638–2646. Association for Computational Linguistics (2018)

    Google Scholar 

  15. Cooper, A.: The Inmates Are Running the Asylum: Why High Tech Products Drive Us Crazy and How to Restore the Sanity, 1st edn. Sams - Pearson Education, Indianapolis (1999)

    Google Scholar 

  16. Cummings, P., Mullins, R., Moquete, M., Schurr, N.: Hello World! I am Charlie, an Artificially Intelligent Conference Panelist (2021). http://hdl.handle.net/10125/70656. Accessed 01 Sept 2022

  17. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  18. Dinan, E., et al.: The second conversational intelligence challenge (ConvAI2) (2019). http://arxiv.org/abs/1902.00098

  19. Fadel, A., Al-Ayyoub, M., Cambria, E.: Justers at semeval-2020 task 4: evaluating transformer models against commonsense validation and explanation, p. 535–542 (2020)

    Google Scholar 

  20. Garima, L.F., Kale, S., Sundararajan, M.: Estimating training data influence by tracing gradient descent. In: NIPS 2020, pp. 19920–19930. Curran Associates Inc., Red Hook (2020). Accessed 01 Sept 2022

    Google Scholar 

  21. Grudin, J.: Why personas work: the psychological evidence. In: Pruitt, J., Adlin, T. (eds.) The Persona Lifecycle, pp. 642–663. Elsevier (2006). https://linkinghub.elsevier.com/retrieve/pii/B9780125662512500137. https://doi.org/10.1016/B978-012566251-2/50013-7

  22. Grudin, J., Pruitt, J.: Personas, participatory design and product development: an infrastructure for engagement, p. 8. Sweden (2002)

    Google Scholar 

  23. Holzinger, A., Kargl, M., Kipperer, B., Regitnig, P., Plass, M., Müller, H.: Personas for artificial intelligence (AI) an open source toolbox. IEEE Access 10, 23732–23747 (2022). https://doi.org/10.1109/ACCESS.2022.3154776

    Article  Google Scholar 

  24. Hwang, S., Kim, B., Lee, K.: A data-driven design framework for customer service chatbot. In: Marcus, A., Wang, W. (eds.) HCII 2019. LNCS, vol. 11583, pp. 222–236. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23570-3_17

    Chapter  Google Scholar 

  25. Jansen, B., Salminen, J., Jung, S.G., Guan, K.: Data-Driven Personas, Synthesis Lectures on Human-Centered Informatics, vol. 14, 1st edn. Morgan & Claypool Publishers, San Rafael (2021)

    Google Scholar 

  26. Jansen, B.J., Jung, S.G., Salminen, J.: Employing large language models in survey research. Natural Lang. Process. J. 100020 (2023). https://doi.org/10.1016/j.nlp.2023.100020. https://www.sciencedirect.com/science/article/pii/S2949719123000171

  27. Jansen, B.J., Jung, S.G., Nielsen, L., Guan, K.W., Salminen, J.: How to create personas: Three persona creation methodologies with implications for practical employment. Pac. Asia J. Assoc. Inf. Syst. 14(3) (2022). https://doi.org/10.17705/1pais.14301. https://aisel.aisnet.org/pajais/vol14/iss3/1

  28. Jansen, B.J., Jung, S.G., Salminen, J.: Finetuning analytics information systems for a better understanding of users: evidence of personification bias on multiple digital channels. Inf. Syst. Front., 1–24 (2023)

    Google Scholar 

  29. Jiang, H., Zhang, X., Cao, X., Kabbara, J., Roy, D.: Personallm: investigating the ability of gpt-3.5 to express personality traits and gender differences. arXiv preprint arXiv:2305.02547 (2023)

  30. Jung, S.G., Salminen, J., Jansen, B.J.: Giving faces to data: creating data-driven personas from personified big data. In: IUI 2020, pp. 132–133. Association for Computing Machinery, Cagliari (2020). https://doi.org/10.1145/3379336.3381465. Accessed 29 Apr 2020

  31. Jung, S.G., Salminen, J., Kwak, H., An, J., Jansen, B.J.: Automatic persona generation (apg): a rationale and demonstration. In: CHIIR 2018, pp. 321–324. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3176349.3176893. Accessed 01 Sept 2022

  32. Kim, B., et al.: Interpretability beyond feature attribution: quantitative testing with concept activation vectors (tcav), pp. 2668–2677. PMLR (2018). https://proceedings.mlr.press/v80/kim18d.html. iSSN: 2640-3498

  33. Korsgaard, D., Bjørner, T., Sørensen, P.K., Burelli, P.: Creating user stereotypes for persona development from qualitative data through semi-automatic subspace clustering. User Model. User-Adap. Inter. 30(1), 81–125 (2020). https://doi.org/10.1007/s11257-019-09252-5

    Article  Google Scholar 

  34. Li, Y., Kazemeini, A., Mehta, Y., Cambria, E.: Multitask learning for emotion and personality traits detection. Neurocomputing 493, 340–350 (2022)

    Article  Google Scholar 

  35. Liang, B., Su, H., Gui, L., Cambria, E., Xu, R.: Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowl.-Based Syst. 235, 107643 (2022)

    Article  Google Scholar 

  36. Liao, Q.V., Gruen, D., Miller, S.: Questioning the ai: informing design practices for explainable ai user experiences, pp. 1–15 (2020)

    Google Scholar 

  37. Liu, H., Yin, Q., Wang, W.Y.: Towards explainable nlp: a generative explanation framework for text classification. arXiv:1811.00196 (2018)

  38. Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. arXiv:1907.11692 [cs] (2019). arXiv: 1907.11692

  39. Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions, vol. 30. Curran Associates, Inc. (2017). https://papers.nips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html. Accessed 01 Sept 2022

  40. Lutkevich, B., Burns, E.: What is natural language processing? an introduction to nlp (2021). https://www.techtarget.com/searchenterpriseai/definition/natural-language-processing-NLP, Accessed 01 Sept 2022

  41. Madsen, S., Nielsen, L.: Exploring persona-scenarios - using storytelling to create design ideas. In: Human Work Interaction Design: Usability in Social, Cultural and Organizational Contexts, pp. 57–66. IFIP Advances in Information and Communication Technology (2010). https://doi.org/10.1007/978-3-642-11762-6_5

  42. Maryland, O.G.C.F.S.W.U., Baltimore, S.O.S.W.W.R.S., Email, A.M.P.E.S.U.: Grand challenges for social work (2022). https://grandchallengesforsocialwork.org/about/history/. Accessed 01 Sept 2022

  43. Matthews, T., Judge, T., Whittaker, S.: How do designers and user experience professionals actually perceive and use personas?. In: The 2012 ACM Annual Conference p. 1219. ACM Press, Austin (2012). https://doi.org/10.1145/2207676.2208573. http://dl.acm.org/citation.cfm?doid=2207676.2208573. Accessed 31 Mar 2020

  44. McGinn, J.J., Kotamraju, N.: Data-driven persona development, p. 1521–1524. ACM, Florence (2008). https://doi.org/10.1145/1357054.1357292

  45. Miaskiewicz, T., Kozar, K.A.: Personas and user-centered design: how can personas benefit product design processes? Des. Stud. 32(5), 417–430 (2011)

    Article  Google Scholar 

  46. Miaskiewicz, T., Sumner, T., Kozar, K.A.: A latent semantic analysis methodology for the identification and creation of personas, pp. 1501–1510. ACM (2008). http://dl.acm.org/citation.cfm?id=1357290

  47. Minichiello, A., Hood, J.R., Harkness, D.S.: Bringing user experience design to bear on stem education: a narrative literature review. J. STEM Educ. Res. 1(1–2), 7–33 (2018)

    Article  Google Scholar 

  48. Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)

    Article  MathSciNet  Google Scholar 

  49. Mueller, S.T., Hoffman, R.R., Clancey, W.J., Emery, A.K., Klein, G.: Explanation in human-ai systems: a literature meta-review synopsis of key ideas and publications and bibliography for explainable ai. Technical report (2019). https://apps.dtic.mil/sti/citations/AD1073994

  50. Nielsen, L.: Engaging personas and narrative scenarios, PhD Series, vol. 17. Samfundslitteratur (2004)

    Google Scholar 

  51. Nielsen, L., Hansen, K.S., Stage, J., Billestrup, J.: A template for design personas: Analysis of 47 persona descriptions from Danish industries and organizations. Int. J. Sociotechnol. Knowl. Dev. 7(1), 45–61 (2015). https://doi.org/10.4018/ijskd.2015010104

    Article  Google Scholar 

  52. Nielsen, L., Storgaard, H.K.: Personas is applicable: a study on the use of personas in Denmark, pp. 1665–1674. ACM (2014)

    Google Scholar 

  53. Pamungkas, E.W., Basile, V., Patti, V.: A joint learning approach with knowledge injection for zero-shot cross-lingual hate speech detection. Inf. Process. Manag. 58(4), 102544 (2021). https://doi.org/10.1016/j.ipm.2021.102544

    Article  Google Scholar 

  54. Priyadarshini, S.B.B., Bagjadab, A.B., Mishra, B.K.: A brief overview of natural language processing and artificial intelligence. In: Natural Language Processing in Artificial Intelligence, p. 14. Apple Academic Press (2020)

    Google Scholar 

  55. Raina, V., Krishnamurthy, S.: Natural language processing. In: Building an Effective Data Science Practice, pp. 63–73. Springer, Heidelberg (2022). https://doi.org/10.1007/978-81-322-3972-7_19

  56. Ribeiro, M.T., Singh, S., Guestrin, C.: “why should i trust you?": explaining the predictions of any classifier. In: KDD 2016, pp. 1135–1144. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2939672.2939778. Accessed 01 Sept 2022

  57. Sai, A.B., Mohankumar, A.K., Khapra, M.M.: A survey of evaluation metrics used for nlg systems. Technical report (2020). Accessed 08 June 2022

    Google Scholar 

  58. Salewski, L., Alaniz, S., Rio-Torto, I., Schulz, E., Akata, Z.: In-context impersonation reveals large language models’ strengths and biases. arXiv preprint arXiv:2305.14930 (2023)

  59. Salminen, J., Guan, K., Jung, S.G., Jansen, B.J.: A survey of 15 years of data-driven persona development. Int. J. Human-Comput. Interact. 37(18), 1685–1708 (2021). https://doi.org/10.1080/10447318.2021.1908670

    Article  Google Scholar 

  60. Salminen, J., Jansen, B.J., An, J., Kwak, H., Jung, S.G.: Are personas done? evaluating their usefulness in the age of digital analytics. Pers. Stud. 4(2), 47–65 (2018). https://doi.org/10.21153/psj2018vol4no2art737

    Article  Google Scholar 

  61. Salminen, J., Jung, S.G., An, J., Kwak, H., Nielsen, L., Jansen, B.J.: Confusion and information triggered by photos in persona profiles. Int. J. Human-Comput. Stud. 129, 1–14 (2019). https://doi.org/10.1016/j.ijhcs.2019.03.005

    Article  Google Scholar 

  62. Salminen, J., Jung, S.G., Jansen, B.: Developing persona analytics towards persona science. In: 27th International Conference on Intelligent User Interfaces, IUI 2022, pp. 323–344. Association for Computing Machinery (2022). https://doi.org/10.1145/3490099.3511144

  63. Salminen, J., Jung, S.G., Jansen, B.J.: The future of data-driven personas: a marriage of online analytics numbers and human attributes, pp. 596–603. SciTePress, Heraklion (2019). Accessed 22 Aug 2019

    Google Scholar 

  64. Salminen, J., Jung, S.G., Jansen, B.J.: Are data-driven personas considered harmful?: diversifying user understandings with more than algorithms. Pers. Stud. 7(1), 48–63 (2021). iSBN: 2205-5258

    Google Scholar 

  65. Salminen, J., Jung, S.G., Santos, J., Jansen, B.J.: Toxic text in personas: an experiment on user perceptions. AIS Trans. Hum.-Comput. Interact. 13(4), 453–478 (2021). https://doi.org/10.17705/1thci.00157

  66. Salminen, J., Mustak, M., Corporan, J., Jung, S.G., Jansen, B.J.: Detecting pain points from user-generated social media posts using machine learning. J. Interact. Mark. 10949968221095556 (2022). https://doi.org/10.1177/10949968221095556

  67. Salminen, J., Rao, R.G., Jung, S., Chowdhury, S.A., Jansen, B.J.: Enriching social media personas with personality traits: a deep learning approach using the big five classes. In: Degen, H., Reinerman-Jones, L. (eds.) HCII 2020. LNCS, vol. 12217, pp. 101–120. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50334-5_7

    Chapter  Google Scholar 

  68. Salminen, J., Santos, J.M., Kwak, H., An, J., Jung, S.G., Jansen, B.J.: Persona perception scale: development and exploratory validation of an instrument for evaluating individuals’ perceptions of personas. Int. J. Hum.-Comput. Stud. 141, 102437 (2020). https://doi.org/10.1016/j.ijhcs.2020.102437

  69. Shneiderman, B., Plaisant, C., Cohen, M., Jacobs, S., Elmqvist, N., Diakopoulos, N.: Grand challenges for HCI researchers. Interactions 23(5), 24–25 (2016)

    Article  Google Scholar 

  70. Stephanidis, C., et al.: Seven HCI grand challenges. Int. J. Hum.-Comput. Interact. 35(14), 1229–1269 (2019)

    Article  Google Scholar 

  71. Stevenson, P.D., Mattson, C.A.: The personification of big data. In: Proceedings of the Design Society: International Conference on Engineering Design, vol. 1. no. 1, pp. 4019–4028 (2019). https://doi.org/10.1017/dsi.2019.409

  72. Tan, H., Peng, S., Liu, J.X., Zhu, C.P., Zhou, F.: Generating personas for products on social media: a mixed method to analyze online users. Int. J. Hum.-Comput. Interact. 38(13), 1255–1266 (2021). https://doi.org/10.1080/10447318.2021.1990520

    Article  Google Scholar 

  73. Turing, A.M.: Computing machinery and intelligence. Mind 59(236), 433–460 (1950)

    Article  MathSciNet  Google Scholar 

  74. Volkova, S., Wilson, T., Yarowsky, D.: Exploring demographic language variations to improve multilingual sentiment analysis in social media. In: EMNLP 2013, pp. 1815–1827. Association for Computational Linguistics, Seattle (2013). https://www.aclweb.org/anthology/D13-1187. Accessed 27 Dec 2019

  75. Wood-Doughty, Z., Shpitser, I., Dredze, M.: Generating synthetic text data to evaluate causal inference methods. Technical report (2021). http://arxiv.org/abs/2102.05638. https://doi.org/10.48550/arXiv.2102.05638

  76. Zhang, X., Brown, H.F., Shankar, A.: Data-driven personas: constructing archetypal users with clickstreams and user telemetry. In: CHI 2016, pp. 5350–5359. ACM, San Jose (2016). Accessed 04 Nov 2017

    Google Scholar 

  77. Zhu, H., Wang, H., Carroll, J.M.: Creating persona skeletons from imbalanced datasets - a case study using U.S. older adults’ health data. In: DIS 2019, pp. 61–70. ACM, New York (2019). https://doi.org/10.1145/3322276.3322285. Accessed 01 Dec 2021

Download references

Author information

Authors and Affiliations

  1. School of Marketing and Communication, University of Vaasa, Vaasa, Finland

    Joni Salminen

  2. Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar

    Soon-gyo Jung & Bernard Jansen

  3. Qatar National Research Fund, Doha, Qatar

    Hind Almerekhi

  4. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

Authors
  1. Joni Salminen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Soon-gyo Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hind Almerekhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Erik Cambria
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bernard Jansen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joni Salminen .

Editor information

Editors and Affiliations

  1. Siemens Corporation, Princeton, NJ, USA

    Helmut Degen

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

  3. San Jose State University, San Jose, CA, USA

    Abbas Moallem

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Salminen, J., Jung, Sg., Almerekhi, H., Cambria, E., Jansen, B. (2023). How Can Natural Language Processing and Generative AI Address Grand Challenges of Quantitative User Personas?. In: Degen, H., Ntoa, S., Moallem, A. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14059. Springer, Cham. https://doi.org/10.1007/978-3-031-48057-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-48057-7_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48056-0

  • Online ISBN: 978-3-031-48057-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation