Abstract
This study explores the trends in Human-Machine Communication (HMC) scholarship in the past decade. We examined 444 peer-reviewed empirical studies published between 2010 and 2021 across journals with highest impact factor according to Social Sciences Citation Index (SSCI). Through a systematic review, we looked at theoretical frameworks, methodological approaches, studied technologies, funding sources, and contributing countries in HMC studies. Using an LDA topic modeling on article abstracts, we further explore the top topic composition in the field and topic distribution across the journals in the past decade. Our analysis revealed diversity among contributing countries. The United States-led studies saw the highest share in HMC research, followed by Asia and Europe. Funding saw a dominant contribution from government and university. A diversity in thematic focus was observed with some topics' dominance among domain-specific journals. Significant differences among journals in terms of theory, method, investigated technology and contributing disciplinary affiliation were also found.
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References
Fortunati, L., Edwards, A.: Moving Ahead with Human-Machine Communication (2021)
Guzman, A.L., Lewis, S.C.: Artificial intelligence and communication: a human-machine communication research agenda. New Media Soc. 22(1), 70–86 (2020). https://doi.org/10.1177/1461444819858691
Song, H., Eberl, J.-M., Eisele, O.: Less fragmented than we thought? Toward clarification of a subdisciplinary linkage in communication science, 2010–2019. J. Commun. 70(3), 310–334 (2020). https://doi.org/10.1093/joc/jqaa009
Duke, N.K., Mallette, M.H.: Critical Issues: Preparation for New Literacy Researchers in Multi-Epistemological, Multi-Methodological Times (2001). https://doi.org/10.1080/10862960109548114
Guzman, A.L.: Ontological boundaries between humans and computers and the implications for Human-Machine Communication. Hum.-Machine Commun. 1, 37–54 (2020). https://doi.org/10.30658/hmc.1.3
Johanssen, J., Wang, X.: Artificial intuition in tech journalism on AI: imagining the human subject. Hum.-Machine Commun. 2, 173–190 (2021). https://doi.org/10.30658/hmc.2.9
Wołk, K.: Emergency, pictogram-based augmented reality medical communicator prototype using precise eye-tracking technology. Cyberpsychol. Behav. Soc. Netw. 22(2), 151–157 (2019). https://doi.org/10.1089/cyber.2018.0035
Guo, F., Li, M., Qu, Q., Duffy, V.G.: The effect of a humanoid robot’s emotional behaviors on users’ emotional responses: evidence from pupillometry and electroencephalography measures. Int. J. Hum.-Comput. Interaction 35(20), 1947–1959 (2019). https://doi.org/10.1080/10447318.2019.1587938
Edwards, C., et al.: Communicating with machines: interventions with digital agents. International Communication Association (ICA) 2017 Pre-Conference (2017)
Gunkel, D.J.: Communication and artificial intelligence: opportunities and challenges for the 21st century. Communication+1, 1(1), 1–25 (2012). https://doi.org/10.7275/R5QJ7F7R
Lewis, S.C., Guzman, A.L., Schmidt, T.R.: Automation, journalism, and human–machine communication: rethinking roles and relationships of humans and machines in news. Digit. J. 7(4), 409–427 (2019). https://doi.org/10.1080/21670811.2019.1577147
Pavitt, C., Braddock, K., Mann, A.: Group communication during resource dilemmas: 3. Effects of social value orientation. Commun. Quarterly 57(4), 433–451 (2009). https://doi.org/10.1080/01463370903320856
Dautenhahn, K.: Socially intelligent agents in human primate culture. In: Payr, S., Trappl, R. (eds.) Agent Culture: Human-Agent Interaction in a Multicultural World, pp. 35–51. CRC Press (2004). https://doi.org/10.1201/b12476
Edwards, A.P.: Animals, humans, and machines: interactive implications of ontological classification. In: Guzman, A.L. (ed.), Human-Machine Communication: Rethinking Communication, Technology, and Ourselves, pp. 29–50. Peter Lang (2018). https://doi.org/10.3726/b14399
Guzman, A.L.: Human-Machine Communication. https://www.peterlang.com/document/1055458 (2018)
Sundar, S.S.: The MAIN model: a heuristic approach to understanding technology effects on credibility. In: Metzger, M.J., Flanagin, A.J. (eds.), MacArthur Foundation Series on Digital Media and Learning, pp. 73–100. Cambridge (2008). https://doi.org/10.1162/dmal.9780262562324.073
Smith, R.G., Eckroth, J.: Building AI applications: yesterday, today, and tomorrow. AI Mag. 38(1), 6–22 (2017). https://doi.org/10.1609/aimag.v38i1.2709
Human-Machine Communication: https://stars.library.ucf.edu/hmc/ (2020)
Lombard, M., Ditton, T.: At the heart of it all: the concept of presence. J. Comput.-Mediated Commun. 3(2), JCMC321 (1997)
Nass, C., Moon, Y.: Machines and mindlessness: social responses to computers. J. Soc. Issues 56(1), 81–103 (2000)
Sundar, S.S., Nass, C.: Source orientation in human-computer interaction: programmer, networker, or independent social actor. Commun. Res. 27(6), 683–703 (2000)
Picard, R.W.: Affective computing: challenges. Int. J. Hum. Comput. Stud. 59(1), 55–64 (2003). https://doi.org/10.1016/S1071-5819(03)00052-1
Palandrani, P., Little, A.: A decade of change: how Tech evolved in the 2010s and what’s in store for the 2020s (2020)
Auxier, B., Anderson, M., Kumar, M.: 10 tech-related trends that shaped the decade. Pew Research Center. https://www.pewresearch.org/fact-tank/2019/12/20/10-tech-related-trends-that-shaped-the-decade/ (2019)
Borah, P.: Emerging communication technology research: theoretical and methodological variables in the last 16 years and future directions. New Media Soc. 19(4), 616–636 (2017). https://doi.org/10.1177/1461444815621512
Tomasello, T.K., Lee, Y., Baer, A.P.: ‘New media’ research publication trends and outlets in communication, 1990–2006. New Media Soc. 12(4), 531–548 (2010). https://doi.org/10.1177/1461444809342762
Page, M.J., et al.: The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst. Rev. 10(89), 1–11 (2021). https://doi.org/10.1186/s13643-021-01626-4
Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. 101(Suppl. 1), 5228–5235 (2004)
Sun, L., Yin, Y.: Discovering themes and trends in transportation research using topic modeling. Transport. Res. C: Emerg. Technol. 77, 49–66 (2017)
Blei, D.M., Ng, A., Jordan, M.: Latent Dirichlet allocation. J. Mach. Learn. Res. 30 (2003)
Panichella, A.: A systematic comparison of search-based approaches for LDA hyperparameter tuning. Inf. Softw. Technol. 130, 106411 (2021). https://doi.org/10.1016/j.infsof.2020.106411
Mimno, D., Wallach, H., Talley, E., Leenders, M., McCallum, A.: Optimizing semantic coherence in topic models. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 262–272. https://aclanthology.org/D11-1024 (2011)
Panichella, A., Dit, B., Oliveto, R., Di Penta, M., Poshynanyk, D., De Lucia, A.: How to effectively use topic models for software engineering tasks? An approach based on genetic algorithms. In: 2013 35th International Conference on Software Engineering (ICSE), pp. 522–531 (2013). https://doi.org/10.1109/ICSE.2013.6606598
Cao, J., Xia, T., Li, J., Zhang, Y., Tang, S.: A density-based method for adaptive LDA model selection. In: Neurocomputing — 16th European Symposium on Artificial Neural Networks, 2008, vol. 72, no. 7–9, pp. 1775–1781 (2009)
Arun, R., Suresh, V., Veni Madhavan, C.E., Narasimha Murthy, M.N.: On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS (LNAI), vol. 6118, pp. 391–402. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13657-3_43
Schwarz, C.: Ldagibbs: a command for topic modeling in Stata using latent Dirichlet allocation. Stata J.: Promot. Commun. Stat. Stata 18(1), 101–117 (2018). https://doi.org/10.1177/1536867X1801800107
Shibuya, Y., Hamm, A., Pargman, T.C.: Mapping HCI Research Methods for Studying Social Media Interaction: A Systematic Literature Review. https://www.sciencedirect.com/science/article/pii/S0747563221004544 (2022)
Collum, M.: The State of UX Research in 2019. Medium. https://uxdesign.cc/the-state-of-ux-research-in-2019-4ba797c09b2f (31 Jan 2019)
Nielsen, J.: A 100-Year View of User Experience. Nielsen Norman Group. https://www.nngroup.com/articles/100-years-ux/ (2017)
Computers in Human Behavior Journal, Aims & Scope, Elsevier: https://www.journals.elsevier.com/journals.elsevier.com/computers-in-human-behavior
Castro, D., McLaughlin, M., Chivot, E.: Who Is Winning the AI Race: China, the EU or the United States? 106 (2021)
AI Report: Artificial Intelligence: How knowledge is created, transferred, and used Trends in China, Europe, and the United States. Elsevier. https://www.elsevier.com/research-intelligence/resource-library/ai-report (n.d.). Retrieved 10 Feb 2022
Member States and Commission to work together to boost artificial intelligence “made in Europe”: European Commission – European Commission. https://ec.europa.eu/commission/presscorner/detail/en/IP_18_6689 (n.d.). Retrieved 10 Feb 2022
Techleap.nl.: Netherlands Emerging as “Hottest Tech Hub” in Europe, but not Reaching its Full Potential yet. https://www.prnewswire.com/news-releases/netherlands-emerging-as-hottest-tech-hub-in-europe-but-not-reaching-its-full-potential-yet-301438925.html (n.d.). Retrieved 10 Feb 2022
Li, D., Tong, T.W., Xiao, Y.: Is China Emerging as the Global Leader in AI? Harvard Business Review. https://hbr.org/2021/02/is-china-emerging-as-the-global-leader-in-ai (18 Feb 2021)
Global Research and Development Expenditures: Fact Sheet 2022 [online]. Congressional Research Service. https://sgp.fas.org/crs/misc/R44283.pdf
Kose, M.A., Sugawara, N., Terrones, M.E.: Global Recessions. World Bank (2020). https://doi.org/10.1596/1813-9450-9172
Johnson, C.: Most Americans are wary of industry-funded research. Pew Research Center. https://www.pewresearch.org/fact-tank/2019/10/04/most-americans-are-wary-of-industry-funded-research/ (2019). Retrieved 7 Feb 2022
Fabbri, A., Lai, A., Grundy, Q., Bero, L.A.: The influence of industry sponsorship on the research agenda: a scoping review. Am. J. Public Health 108(11), e9–e16 (2018). https://doi.org/10.2105/AJPH.2018.304677
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Makady, H., Liu, F. (2022). The Status of Human-Machine Communication Research: A Decade of Publication Trends Across Top-Ranking Journals. In: Kurosu, M. (eds) Human-Computer Interaction. Theoretical Approaches and Design Methods. HCII 2022. Lecture Notes in Computer Science, vol 13302. Springer, Cham. https://doi.org/10.1007/978-3-031-05311-5_6
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