Facilitating Discussion on COVID-19 with Autonomous Facilitator: A Case Study on the Comparison of Expert Discussion versus the Public Paradigm Ratio of Reply for Agent Facilitated Post
DOI:
https://doi.org/10.14456/jiist.2022.19Keywords:
Conversational agent, online discussion, public paradigm, expert paradigm, COVID-19, artificial intelligence, online forumAbstract
An important way to promote large-scale online debate development and improve discussion environment is discussion support platform, with artificial intelligence-based facilitation being its key part. To support such debates, software agents as facilitators need to be developed to facilitate these discussions. In this study, we propose to study this phenomenon using an online-debate system based on facilitation called D-Agree. We aimed to investigate the influence of software entities as autonomous facilitators(AF) on the evaluation of online debate involving cross-class people on COVID-19-related discussion in Afghanistan. This study was conducted with two classes of people:(1) health workers (n= 16) as experts on COVID-19 related debate, and(2) private citizens(n= 16) as public and non-experts on COVID-19-related discussions. Initially the health workers were selected using a non-probability sampling technique of convenience sampling survey in collaboration with Afghanistan national public health institute, and the private citizens were selected using convenience sampling, and then we used stratified random sampling to select 16 people from each class. We created 8 online groups, four for each class namely, A~D, and randomly assigned subjects of each class to a group based on a female and three male members(n= 4; female= 1 and male= 3). The agent will dynamically interact with participants of each group or class of people based on predefined facilitation ratio(1:2= A& C groups; 1:3 B& D groups). For the sake of experimental evolution, we used discussion annotated datasets that contain human and AF posts, and the number of human posts towards AF posts. According to the results, agents with a facilitation threshold of two people(1:2) had a significant impact on discussion development in terms of both discussion elements and posted characters compared with facilitation threshold of 3 people(1:3). With 1:2 setting, we found that the agent improved the responsiveness of both expert class and public class(A&C groups) than 1:3 setting(B&D groups). That means Afghans engage more-write more characters with 1:2 than 1:3 with agent-based facilitation. Hence, the agent increased the number of identified discussion elements. The output of this research can be used as a precondition on setting agent facilitation for least development countries like Afghanistan.
References
Haqbeen, J., Ito, T., Sahab, S., Sato, T., Okuhara, S., Hofiani, M.: Insights from a large-scale discussion on COVID-19 in collective intelligence. In: IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology(WI-IAT20), (2020)
Wyss, D., Beste, S.: Artificial facilitation: promoting collective reasoning within asynchronous discussions, Journal of Information Technology& Politics, 14,3. 214--231(2017)
Ito, T., Shibata, D., Suzuki, S., Yamaguchi, N., Nishida, T., Hiraishi, K., Yoshino, K.: Agent that facilitates crowd discussion. In: ACM Collective Intelligence Conference,(2019)
Haqbeen, J., Ito, T., Hadfi, R., Nishida, T., Sahab, Z., Sahab, S., Roghmal, S., Amiryar, R.: Promoting discussion with AI-based facilitation: Urban dialogue with Kabul City. In: ACM Collective Intelligence Conference(2020)
Tavanapour, N., Poser, M., Bittner, E.A.C.: Supporting the Idea Generation Process in Citizen Participation- toward an Interactive System with a Conversational Agent as Facilitator. In: 27th European Conference on Information Systems, Stockholm and Uppsala.(2019).
UNHABITAT Homepage, https://unhabitat.org/sites/default/files/download-manager-files/State%20of%20Afghan%20Cities%202015%20Volume_1.pdf , last accessed 2021/09/1
Kabul Municipality Official website, accessed at: https://km.gov.af , last accessed 2021/09/1
CMS. The Largest Cities in the World and Their Mayors. World Mayors (City Mayors Statistics), http://www.citymayors.com/statistics/largest-cities-mayors-1.html , last accessed 2021/09/1
National Statistic and Information Authority(NSIA). April 2021. Accessed at: https://www.nsia.gov.af:8080/wp-content/uploads/2021/06/Estimated-Population-of-Afghanistan1-1400.pdf , last accessed 2021/09/1
Thompson, S.K.: Sampling. third edition, John Wiley& Sons, Inc., chapter 13(2012), 171--175,(2012)
Matsuyama, Y., Akiba, I., Fujie, S., Kobayashi, T.: Four-participant group conversation: A facilitation robot controlling engagement density as the fourth participant, Computer Speech& Language, 33.1, 1--24(2015).
Haqbeen, Jawad; Sahab, S.; Ito, T.; and Rizzi, P. Using decision support system to enable crowd identify neighborhood issues and its solutions for policy makers: An online experiment at Kabul municipal level. Sustainability, 13(10), 5453(2021).
Haqbeen, Jawad; Sahab, S.; Ito, T. AI-based mediation improves opinion solicitation in a large-scale online discussion: Experimental evidence from Kabul Municipality, IJCAI Workshop on AI for Social Good, 2021.
Haqbeen, J.; Ito, T.; Hadfi, R.; Sahab, Z.; Sahab, S.; Amiryar, R.; Nishida, T. Usage& Application of AI-based Discussion Facilitation System for Urban Renewal in Selected Districts of Kabul City: Afghanistan Experimental View. In Proceedings of the 34th Annual Conference of the Japanese Society for Artificial Intelligence, Kumamoto, Japan, 9–12 June 2020; pp. 1–4.
Ito, T. Towards agent-based large-scale decision support system: The effect of facilitator, Proceedings of the 51st Hawaii International Conference on System Sciences, 2018.
Ito, T.; Suzuki, S.; Yamaguchi, N.; Nishida, T.; Hiraishi, K.; and Yoshino, K. Crowd Discussion for the Future of Your Hometown: An Agent-based Crowd Discussion Support System, Proceedings of the 2019 Workshop on Artificial Intelligence and United Nations Sustainable Development Goals(AI4SDGs), Macao, 2019.
Ito, T.; Hadfi, R.; Haqbeen, J.; Suzuki, S.; Sakai, A.; Kawamura, N.; Yamaguchi, N. Agent-Based Crowd Discussion Support System and Its Societal Experiments. In Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness, 1st ed.; Demazeau, Y., Holvoet, T., Corchado, J., Costantini, S., Eds.; Springer: L’Aquila, Italy, 2020; pp. 430–433.
Hadfi, R.; Haqbeen, J.; Sahab, S.; and Ito, T. Argumentative Conversational Agents for Online Discussions, Journal of Systems Science and Systems Engineering. 1-15(2021).
Kunz, W.; Rittlel, H.W. Issues as elements of information systems. Tech. Rep. working paper no. 131 1970.
Ganasegeran, K.; Abdulrahman, S.A. Artificial Application in tracking health behaviors during disease epidemics. In proceeding Human Bavaiour Analysis using Intelligent Systems, Cham Springer, pages 141-155, 2020.
Hou,Z.; Du, F.; Jiang, H.; Zhou, X.; Lin, L. Assessment of public attention, risk perception, emotiannal and behavioural responses to the COVID-19 outbreak: social medial surveillance in China. medRxiv preprint medRxiv: doi: https://doi.org/10.1101/2020.03.14.20035956
Ackerman, D.S.; Gross, B.L. Synchronous online discussion board as a primary mode of delivering marketing education: responding to the Covid-19 pandemic and beyond. Marketing Education Review, DOI: 10.1080/10528008.2021.1893752
Haqbeen, J.; Ito, T.; Sahab, S.; Hadfi, R.; Okuhara, S.; Saba, N.; Hofiani, M.; Baregzai, U. A contribution to covid-19 prevention through crowd collaboration using conversational AI& social platforms. AI for Social Good Workshop.
Savolainen, R. Assessing the credibility of Covid-19 vaccine disinformation in online discussion. Journal of Information Science, 2021.
Xue J, Chen J, Hu R, Chen C, Zheng C, Su Y, Zhu T Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach. Journal of Medical Internet Research, 2020;22(11):e20550.
Jelodar, H.; Wang, y.; Orji, R.; Huang, H. Deep sentiment classification and topic discover on novel coronavirus or covid-19 online discussions: NLP using lstm recurrent neural network approach. IEEE Journal of Biomedical and Health Informatics, 24.8, 2733-2742(2020).
French, M.; Popal, A.; Rahimi, H.; Popure, S.; Turkstra, J. Institutionalizing Participatory Slum Upgrading: A Case Study of Urban Co-production from Afghanistan, 2002–2016. Environ. Urban. 2018, 31, 209–230.
