skip to main content
10.1145/3581641.3584031acmconferencesArticle/Chapter ViewAbstractPublication PagesiuiConference Proceedingsconference-collections
research-article
Open access

Powering an AI Chatbot with Expert Sourcing to Support Credible Health Information Access

Published: 27 March 2023 Publication History
  • Get Citation Alerts
  • Abstract

    During a public health crisis like the COVID-19 pandemic, a credible and easy-to-access information portal is highly desirable. It helps with disease prevention, public health planning, and misinformation mitigation. However, creating such an information portal is challenging because 1) domain expertise is required to identify and curate credible and intelligible content, 2) the information needs to be updated promptly in response to the fast-changing environment, and 3) the information should be easily accessible by the general public; which is particularly difficult when most people do not have the domain expertise about the crisis. In this paper, we presented an expert-sourcing framework and created Jennifer, an AI chatbot, which serves as a credible and easy-to-access information portal for individuals during the COVID-19 pandemic. Jennifer was created by a team of over 150 scientists and health professionals around the world, deployed in the real world and answered thousands of user questions about COVID-19. We evaluated Jennifer from two key stakeholders’ perspectives, expert volunteers and information seekers. We first interviewed experts who contributed to the collaborative creation of Jennifer to learn about the challenges in the process and opportunities for future improvement. We then conducted an online experiment that examined Jennifer’s effectiveness in supporting information seekers in locating COVID-19 information and gaining their trust. We share the key lessons learned and discuss design implications for building expert-sourced and AI-powered information portals, along with the risks and opportunities of misinformation mitigation and beyond.

    Supplementary Material

    PDF File (cscw22b-sub3006-i55 (1).pdf)
    Supplementary Material for Powering an AI Chatbot with Expert Sourcing to Support Credible Health Information Access

    References

    [1]
    Bill Adair, Chengkai Li, Jun Yang, and Cong Yu. 2017. Progress Toward “the Holy Grail”: The Continued Quest to Automate Fact-Checking. In Proceedings of the 2017 Computation+Journalism Symposium.
    [2]
    Emily M Agree, Abby C King, Cynthia M Castro, Adrienne Wiley, and Dina LG Borzekowski. 2015. “It’s got to be on this page”: Age and cognitive style in a study of online health information seeking. Journal of medical Internet research 17, 3 (2015), e79.
    [3]
    Mabrook S Al-Rakhami and Atif M Al-Amri. 2020. Lies Kill, Facts Save: Detecting COVID-19 Misinformation in Twitter. IEEE Access 8(2020), 155961–155970.
    [4]
    Firoj Alam, Ferda Ofli, and Muhammad Imran. 2018. Processing social media images by combining human and machine computing during crises. International Journal of Human–Computer Interaction 34, 4(2018), 311–327.
    [5]
    Senator Lamar Alexander. 2020. Preparing for the Next Pandemic. https://www.alexander.senate.gov. [Online; accessed 16-June-2020].
    [6]
    Sacha Altay, Anne-Sophie Hacquin, Coralie Chevallier, and Hugo Mercier. 2021. Information delivered by a chatbot has a positive impact on COVID-19 vaccines attitudes and intentions.Journal of Experimental Psychology: Applied(2021).
    [7]
    Cynthia Andrews, Elodie Fichet, Yuwei Ding, Emma S Spiro, and Kate Starbird. 2016. Keeping up with the tweet-dashians: The impact of’official’accounts on online rumoring. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. 452–465.
    [8]
    Ahmer Arif, Kelley Shanahan, Fang-Ju Chou, Yoanna Dosouto, Kate Starbird, and Emma S Spiro. 2016. How information snowballs: Exploring the role of exposure in online rumor propagation. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. 466–477.
    [9]
    Ana I Bento, Thuy Nguyen, Coady Wing, Felipe Lozano-Rojas, Yong-Yeol Ahn, and Kosali Simon. 2020. Evidence from internet search data shows information-seeking responses to news of local COVID-19 cases. Proceedings of the National Academy of Sciences 117, 21(2020), 11220–11222.
    [10]
    Gretchen K Berland, Marc N Elliott, Leo S Morales, Jeffrey I Algazy, Richard L Kravitz, Michael S Broder, David E Kanouse, Jorge A Muñoz, Juan-Antonio Puyol, Marielena Lara, 2001. Health information on the Internet: accessibility, quality, and readability in English and Spanish. jama 285, 20 (2001), 2612–2621.
    [11]
    Michael S Bernstein, Greg Little, Robert C Miller, Björn Hartmann, Mark S Ackerman, David R Karger, David Crowell, and Katrina Panovich. 2010. Soylent: a word processor with a crowd inside. In Proceedings of the 23nd annual ACM symposium on User interface software and technology. 313–322.
    [12]
    Timothy W Bickmore, Dina Utami, Robin Matsuyama, and Michael K Paasche-Orlow. 2016. Improving access to online health information with conversational agents: a randomized controlled experiment. Journal of medical Internet research 18, 1 (2016), e1.
    [13]
    Leticia Bode and Emily K Vraga. 2018. See something, say something: Correction of global health misinformation on social media. Health communication 33, 9 (2018), 1131–1140.
    [14]
    Maged N Kamel Boulos, Bernd Resch, David N Crowley, John G Breslin, Gunho Sohn, Russ Burtner, William A Pike, Eduardo Jezierski, and Kuo-Yu Slayer Chuang. 2011. Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples. International journal of health geographics 10, 1 (2011), 1–29.
    [15]
    Josip Bozic, Oliver A Tazl, and Franz Wotawa. 2019. Chatbot testing using AI planning. In 2019 IEEE International Conference On Artificial Intelligence Testing (AITest). IEEE, 37–44.
    [16]
    J Scott Brennen, Felix Simon, Philip N Howard, and Rasmus Kleis Nielsen. 2020. Types, sources, and claims of COVID-19 misinformation. Reuters Institute 7(2020), 3–1.
    [17]
    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.). Vol. 33. Curran Associates, Inc., 1877–1901. https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
    [18]
    Leonardo Bursztyn, Aakaash Rao, Christopher Roth, and David Yanagizawa-Drott. 2020. Misinformation during a pandemic. University of Chicago, Becker Friedman Institute for Economics Working Paper2020-44(2020).
    [19]
    Jiyoung Chae. 2016. Who avoids cancer information? Examining a psychological process leading to cancer information avoidance. Journal of health communication 21, 7 (2016), 837–844.
    [20]
    Miao Chao, Dini Xue, Tour Liu, Haibo Yang, and Brian J Hall. 2020. Media use and acute psychological outcomes during COVID-19 outbreak in China. Journal of Anxiety Disorders 74 (2020), 102248.
    [21]
    Chatfuel. 2020. [Online; accessed June-2020].
    [22]
    Yan Chen, Steve Oney, and Walter S Lasecki. 2016. Towards providing on-demand expert support for software developers. In Proceedings of the 2016 CHI conference on human factors in computing systems. 3192–3203.
    [23]
    Seonhwa Choi and Byunggul Bae. 2015. The real-time monitoring system of social big data for disaster management. In Computer science and its applications. Springer, 809–815.
    [24]
    Mark É Czeisler, Michael A Tynan, Mark E Howard, Sally Honeycutt, Erika B Fulmer, Daniel P Kidder, Rebecca Robbins, Laura K Barger, Elise R Facer-Childs, Grant Baldwin, 2020. Public attitudes, behaviors, and beliefs related to COVID-19, stay-at-home orders, nonessential business closures, and public health guidance—United States, New York City, and Los Angeles, May 5–12, 2020. Morbidity and Mortality Weekly Report 69, 24 (2020), 751.
    [25]
    Dialogflow. 2020. [Online; accessed June-2020].
    [26]
    James Price Dillard, Ruobing Li, and Chun Yang. 2020. Fear of Zika: Information seeking as cause and consequence. Health Communication(2020), 1–11.
    [27]
    Mary T Dzindolet, Scott A Peterson, Regina A Pomranky, Linda G Pierce, and Hall P Beck. 2003. The role of trust in automation reliance. International journal of human-computer studies 58, 6 (2003), 697–718.
    [28]
    Eamonn Fahy, Rohan Hardikar, Adrian Fox, and Sean Mackay. 2014. Quality of patient health information on the Internet: reviewing a complex and evolving landscape. The Australasian medical journal 7, 1 (2014), 24.
    [29]
    Julian J Faraway. 2016. Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. CRC press.
    [30]
    Asbjørn Følstad and Petter Bae Brandtzæg. 2017. Chatbots and the new world of HCI. interactions 24, 4 (2017), 38–42.
    [31]
    Allen Foster. 2004. A nonlinear model of information-seeking behavior. Journal of the American society for information science and technology 55, 3 (2004), 228–237.
    [32]
    Mingkun Gao, Ziang Xiao, Karrie Karahalios, and Wai-Tat Fu. 2018. To label or not to label: The effect of stance and credibility labels on readers’ selection and perception of news articles. Proceedings of the ACM on Human-Computer Interaction 2, CSCW(2018), 1–16.
    [33]
    Eun Go and S Shyam Sundar. 2019. Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior 97 (2019), 304–316.
    [34]
    Miaomiao Gong, Yuling Sun, and Liang He. 2019. A Social Network Engaged Crowdsourcing Framework for Expert Tasks. In 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 249–254.
    [35]
    Justine Gunderson, Dwayne Mitchell, Keshia Reid, and Melissa Jordan. 2021. Peer Reviewed: COVID-19 Information-Seeking and Prevention Behaviors in Florida, April 2020. Preventing Chronic Disease 18 (2021).
    [36]
    Yuanyuan Guo, Zhenzhen Lu, Haibo Kuang, and Chaoyou Wang. 2020. Information avoidance behavior on social network sites: Information irrelevance, overload, and the moderating role of time pressure. International Journal of Information Management 52 (2020), 102067.
    [37]
    Christine Hagar. 2015. Crisis informatics. In Encyclopedia of Information Science and Technology, Third Edition. IGI Global, 1350–1358.
    [38]
    E Alison Holman, Dana Rose Garfin, and Roxane Cohen Silver. 2014. Media’s role in broadcasting acute stress following the Boston Marathon bombings. Proceedings of the National Academy of Sciences 111, 1 (2014), 93–98.
    [39]
    Ting-Hao Huang, Joseph Chee Chang, and Jeffrey P Bigham. 2018. Evorus: A crowd-powered conversational assistant built to automate itself over time. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–13.
    [40]
    Amanda Lee Hughes and Andrea H. Tapia. 2015. Social Media in Crisis: When Professional Responders Meet Digital Volunteers. Journal of Homeland Security and Emergency Management 12 (2015). Issue 3.
    [41]
    Juji. 2020. Juji document for chatbot designers. https://docs.juji.io/. [Online; accessed 14-June-2020].
    [42]
    Salil S Kanhere. 2013. Participatory sensing: Crowdsourcing data from mobile smartphones in urban spaces. In International Conference on Distributed Computing and Internet Technology. Springer, 19–26.
    [43]
    Marc-André Kaufhold, Nicola Rupp, Christian Reuter, and Matthias Habdank. 2020. Mitigating information overload in social media during conflicts and crises: design and evaluation of a cross-platform alerting system. Behaviour & Information Technology 39, 3 (2020), 319–342.
    [44]
    Melanie Kellar, Carolyn Watters, and Michael Shepherd. 2007. A field study characterizing Web-based information-seeking tasks. Journal of the American Society for information science and technology 58, 7 (2007), 999–1018.
    [45]
    Marina Kogan and Leysia Palen. 2018. Conversations in the eye of the storm: At-scale features of conversational structure in a high-tempo, high-stakes microblogging environment. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–13.
    [46]
    Walter S Lasecki, Rachel Wesley, Jeffrey Nichols, Anand Kulkarni, James F Allen, and Jeffrey P Bigham. 2013. Chorus: a crowd-powered conversational assistant. In Proceedings of the 26th annual ACM symposium on User interface software and technology. 151–162.
    [47]
    Yi-Chieh Lee, Naomi Yamashita, and Yun Huang. 2020. Designing a chatbot as a mediator for promoting deep self-disclosure to a real mental health professional. Proceedings of the ACM on Human-Computer Interaction 4, CSCW1(2020), 1–27.
    [48]
    Yi-Chieh Lee, Naomi Yamashita, Yun Huang, and Wai Fu. 2020. " I Hear You, I Feel You": Encouraging Deep Self-disclosure through a Chatbot. In Proceedings of the 2020 CHI conference on human factors in computing systems. 1–12.
    [49]
    James R Lewis. 1991. Psychometric evaluation of an after-scenario questionnaire for computer usability studies: the ASQ. ACM Sigchi Bulletin 23, 1 (1991), 78–81.
    [50]
    Xukun Li, Doina Caragea, Cornelia Caragea, Muhammad Imran, and Ferda Ofli. 2019. Identifying disaster damage images using a domain adaptation approach. In Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management.
    [51]
    Yunyao Li, Tyrone Grandison, Patricia Silveyra, Ali Douraghy, Xinyu Guan, Thomas Kieselbach, Chengkai Li, and Haiqi Zhang. 2020. Jennifer for COVID-19: An NLP-Powered Chatbot Built for the People and by the People to Combat Misinformation. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020.
    [52]
    Q Vera Liao, Matthew Davis, Werner Geyer, Michael Muller, and N Sadat Shami. 2016. What can you do? Studying social-agent orientation and agent proactive interactions with an agent for employees. In Proceedings of the 2016 acm conference on designing interactive systems. 264–275.
    [53]
    Zihan Liu, Genta Indra Winata, Zhaojiang Lin, Peng Xu, and Pascale Fung. 2020. Attention-Informed Mixed-Language Training for Zero-Shot Cross-Lingual Task-Oriented Dialogue Systems. In AAAI. AAAI Press, 8433–8440.
    [54]
    Thomas Ludwig, Christoph Kotthaus, Christian Reuter, Sören Van Dongen, and Volkmar Pipek. 2017. Situated crowdsourcing during disasters: Managing the tasks of spontaneous volunteers through public displays. International Journal of Human-Computer Studies 102 (2017), 103–121.
    [55]
    Wenhong Luo and Mohammad Najdawi. 2004. Trust-building measures: a review of consumer health portals. Commun. ACM 47, 1 (2004), 108–113.
    [56]
    Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, and Shrimai Prabhumoye. 2020. Politeness Transfer: A Tag and Generate Approach. In ACL.
    [57]
    Alistair Martin, Jama Nateqi, Stefanie Gruarin, Nicolas Munsch, Isselmou Abdarahmane, Marc Zobel, and Bernhard Knapp. 2020. An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot. Scientific reports 10, 1 (2020), 1–7.
    [58]
    D Harrison McKnight, Vivek Choudhury, and Charles Kacmar. 2002. The impact of initial consumer trust on intentions to transact with a web site: a trust building model. The journal of strategic information systems 11, 3-4 (2002), 297–323.
    [59]
    Lisa M Soederberg Miller and Robert A Bell. 2012. Online health information seeking: the influence of age, information trustworthiness, and search challenges. Journal of aging and health 24, 3 (2012), 525–541.
    [60]
    Adam S Miner, Liliana Laranjo, and A Baki Kocaballi. 2020. Chatbots in the fight against the COVID-19 pandemic. npj Digital Medicine 3, 1 (2020), 1–4.
    [61]
    Clifford Nass, Jonathan Steuer, and Ellen R Tauber. 1994. Computers are social actors. In Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 72–78.
    [62]
    Ida Norheim-Hagtun and Patrick Meier. 2010. Crowdsourcing for crisis mapping in Haiti. Innovations: Technology, Governance, Globalization 5, 4(2010), 81–89.
    [63]
    Kyo-Joong Oh, Dongkun Lee, Byungsoo Ko, and Ho-Jin Choi. 2017. A chatbot for psychiatric counseling in mental healthcare service based on emotional dialogue analysis and sentence generation. In 2017 18th IEEE International Conference on Mobile Data Management (MDM). IEEE, 371–375.
    [64]
    Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, 2022. Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155(2022).
    [65]
    Leysia Palen, Kenneth M Anderson, Gloria Mark, James Martin, Douglas Sicker, Martha Palmer, and Dirk Grunwald. 2010. A vision for technology-mediated support for public participation & assistance in mass emergencies & disasters. ACM-BCS Visions of Computer Science 2010(2010), 1–12.
    [66]
    Leysia Palen, Sarah Vieweg, Jeannette Sutton, Sophia B Liu, and Amanda Hughes. 2007. Crisis informatics: Studying crisis in a networked world. In Proceedings of the Third International Conference on E-Social Science. 7–9.
    [67]
    Archita Pathak and Rohini Srihari. 2019. BREAKING! Presenting Fake News Corpus for Automated Fact Checking. In ACL (Student Research Workshop).
    [68]
    Gordon Pennycook, Jonathon McPhetres, Yunhao Zhang, Jackson G Lu, and David G Rand. 2020. Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention. Psychological science 31, 7 (2020), 770–780.
    [69]
    Marta Poblet, Esteban García-Cuesta, and Pompeu Casanovas. 2018. Crowdsourcing roles, methods and tools for data-intensive disaster management. Information Systems Frontiers 20, 6 (2018), 1363–1379.
    [70]
    Filip Radlinski and Nick Craswell. 2017. A theoretical framework for conversational search. In Proceedings of the 2017 conference on conference human information interaction and retrieval. 117–126.
    [71]
    Daniela Retelny, Michael S Bernstein, and Melissa A Valentine. 2017. No workflow can ever be enough: How crowdsourcing workflows constrain complex work. Proceedings of the ACM on Human-Computer Interaction 1, CSCW(2017), 1–23.
    [72]
    Daniela Retelny, Sébastien Robaszkiewicz, Alexandra To, Walter S Lasecki, Jay Patel, Negar Rahmati, Tulsee Doshi, Melissa Valentine, and Michael S Bernstein. 2014. Expert crowdsourcing with flash teams. In Proceedings of the 27th annual ACM symposium on User interface software and technology. 75–85.
    [73]
    Bram Rochwerg, Rachael Parke, Srinivas Murthy, Shannon M Fernando, Jeanna Parsons Leigh, John Marshall, Neill KJ Adhikari, Kirsten Fiest, Rob Fowler, François Lamontagne, 2020. Misinformation during the coronavirus disease 2019 outbreak: How knowledge emerges from noise. Critical Care Explorations 2, 4 (2020).
    [74]
    Ronald W Rogers. 1983. Cognitive and psychological processes in fear appeals and attitude change: A revised theory of protection motivation. Social psychophysiology: A sourcebook(1983), 153–176.
    [75]
    Corbin Rosset, Chenyan Xiong, Xia Song, Daniel Campos, Nick Craswell, Saurabh Tiwary, and Paul Bennett. 2020. Leading Conversational Search by Suggesting Useful Questions. In Proceedings of The Web Conference 2020. 1160–1170.
    [76]
    Alessandro Rovetta and Akshaya Srikanth Bhagavathula. 2020. COVID-19-related web search behaviors and infodemic attitudes in Italy: Infodemiological study. JMIR public health and surveillance 6, 2 (2020), e19374.
    [77]
    Katherine E Rowan. 1991. When simple language fails: Presenting difficult science to the public. Journal of technical writing and communication 21, 4(1991), 369–382.
    [78]
    Fadi Safieddine, Wassim Masri, and Pardis Pourghomi. 2016. Corporate responsibility in combating online misinformation. International Journal of Advanced Computer Science and Applications (IJACSA) 7, 2(2016), 126–132.
    [79]
    Irina Shklovski, Moira Burke, Sara Kiesler, and Robert Kraut. 2010. Technology adoption and use in the aftermath of Hurricane Katrina in New Orleans. American Behavioral Scientist 53, 8 (2010), 1228–1246.
    [80]
    Madhumita Shrotri, Tui Swinnen, Beate Kampmann, and Edward PK Parker. 2021. An interactive website tracking COVID-19 vaccine development. The Lancet Global Health 9, 5 (2021), e590–e592.
    [81]
    Lee Sproull, Mani Subramani, Sara Kiesler, Janet H Walker, and Keith Waters. 1996. When the interface is a face. Human-computer interaction 11, 2 (1996), 97–124.
    [82]
    Kate Starbird, Grace Muzny, and Leysia Palen. 2012. Learning from the crowd: Collaborative filtering techniques for identifying on-the-ground Twitterers during mass disruptions. In ISCRAM. Citeseer.
    [83]
    Bobby Swar, Tahir Hameed, and Iris Reychav. 2017. Information overload, psychological ill-being, and behavioral intention to continue online healthcare information search. Computers in Human Behavior 70 (2017), 416–425.
    [84]
    Briony Swire-Thompson and David Lazer. 2020. Public health and online misinformation: challenges and recommendations. Annual Review of Public Health 41 (2020), 433–451.
    [85]
    Leila Tavakoli. 2020. Generating Clarifying Questions in Conversational Search Systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 3253–3256.
    [86]
    Thi Tran, Rohit Valecha, Paul Rad, and H Raghav Rao. 2020. An investigation of misinformation harms related to social media during two humanitarian crises. Information systems frontiers(2020), 1–9.
    [87]
    Johanne R Trippas, Damiano Spina, Lawrence Cavedon, Hideo Joho, and Mark Sanderson. 2018. Informing the design of spoken conversational search: Perspective paper. In Proceedings of the 2018 Conference on Human Information Interaction & Retrieval. 32–41.
    [88]
    Jinke D Van Der Laan, Adriaan Heino, and Dick De Waard. 1997. A simple procedure for the assessment of acceptance of advanced transport telematics. Transportation Research Part C: Emerging Technologies 5, 1(1997), 1–10.
    [89]
    Alexandra Vtyurina, Denis Savenkov, Eugene Agichtein, and Charles LA Clarke. 2017. Exploring conversational search with humans, assistants, and wizards. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. 2187–2193.
    [90]
    Joanne I White and Leysia Palen. 2015. Expertise in the wired wild west. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. 662–675.
    [91]
    Ziang Xiao, Sarah Mennicken, Bernd Huber, Adam Shonkoff, and Jennifer Thom. 2021. Let Me Ask You This: How Can a Voice Assistant Elicit Explicit User Feedback?Proceedings of the ACM on Human-Computer Interaction 5, CSCW2(2021), 1–24.
    [92]
    Ziang Xiao, Michelle X Zhou, Wenxi Chen, Huahai Yang, and Changyan Chi. 2020. If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–14.
    [93]
    Ziang Xiao, Michelle X Zhou, Q Vera Liao, Gloria Mark, Changyan Chi, Wenxi Chen, and Huahai Yang. 2020. Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys with Open-ended Questions. ACM Transactions on Computer-Human Interaction (TOCHI) 27, 3(2020), 1–37.
    [94]
    Man-Ching Yuen, Irwin King, and Kwong-Sak Leung. 2011. A survey of crowdsourcing systems. In 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing. IEEE, 766–773.
    [95]
    Michelle X Zhou, Gloria Mark, Jingyi Li, and Huahai Yang. 2019. Trusting virtual agents: The effect of personality. ACM Transactions on Interactive Intelligent Systems (TiiS) 9, 2-3(2019), 1–36.
    [96]
    Fengbin Zhu, Wenqiang Lei, Chao Wang, Jianming Zheng, Soujanya Poria, and Tat-Seng Chua. 2021. Retrieving and reading: A comprehensive survey on open-domain question answering. arXiv preprint arXiv:2101.00774(2021).

    Cited By

    View all
    • (2024)Ethical Considerations for Artificial Intelligence Applications for HIVAI10.3390/ai50200315:2(594-601)Online publication date: 7-May-2024
    • (2024)Empowering Citizens for Climate Adaptation in Norway: Leveraging (AI-Driven) Emerging Technologies2024 9th International Conference on Smart and Sustainable Technologies (SpliTech)10.23919/SpliTech61897.2024.10612554(1-5)Online publication date: 25-Jun-2024
    • (2024)Talk2Care: An LLM-based Voice Assistant for Communication between Healthcare Providers and Older AdultsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596258:2(1-35)Online publication date: 15-May-2024
    • Show More Cited By

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    IUI '23: Proceedings of the 28th International Conference on Intelligent User Interfaces
    March 2023
    972 pages
    ISBN:9798400701061
    DOI:10.1145/3581641
    This work is licensed under a Creative Commons Attribution-NonCommercial International 4.0 License.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 March 2023

    Check for updates

    Author Tags

    1. AI-powered chatbot
    2. COVID-19
    3. crisis informatics
    4. expert sourcing
    5. information access
    6. information seeking
    7. misinformation

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    IUI '23
    Sponsor:

    Acceptance Rates

    Overall Acceptance Rate 746 of 2,811 submissions, 27%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)1,624
    • Downloads (Last 6 weeks)144
    Reflects downloads up to 14 Aug 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Ethical Considerations for Artificial Intelligence Applications for HIVAI10.3390/ai50200315:2(594-601)Online publication date: 7-May-2024
    • (2024)Empowering Citizens for Climate Adaptation in Norway: Leveraging (AI-Driven) Emerging Technologies2024 9th International Conference on Smart and Sustainable Technologies (SpliTech)10.23919/SpliTech61897.2024.10612554(1-5)Online publication date: 25-Jun-2024
    • (2024)Talk2Care: An LLM-based Voice Assistant for Communication between Healthcare Providers and Older AdultsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596258:2(1-35)Online publication date: 15-May-2024
    • (2024)How Can I Signal You To Trust Me: Investigating AI Trust Signalling in Clinical Self-AssessmentsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661612(525-540)Online publication date: 1-Jul-2024
    • (2024)New voices for a better societyProceedings of the National Academy of Sciences10.1073/pnas.2404579121121:18Online publication date: 24-Apr-2024
    • (2024)The Potential of Generative AI Chatbots as Career Advisors in Cybersecurity: Professionals’ PerspectivesInformation Security Education - Challenges in the Digital Age10.1007/978-3-031-62918-1_13(191-206)Online publication date: 11-Jun-2024
    • (2023)Chatbots in Pharmacy: A Boon or a Bane for Patient Care and Pharmacy Practice?Sciences of Pharmacy10.58920/sciphar020300012:3(1-23)Online publication date: 3-Jul-2023
    • (2023)Approaches for the use of Artificial Intelligence in workplace health promotion and prevention: A systematic scoping review (Preprint)JMIR AI10.2196/53506Online publication date: 9-Oct-2023
    • (2023)LLMs for Financial Advisement: A Fairness and Efficacy Study in Personal Decision MakingProceedings of the Fourth ACM International Conference on AI in Finance10.1145/3604237.3626867(100-107)Online publication date: 27-Nov-2023
    • (2023)Inform the Uninformed: Improving Online Informed Consent Reading with an AI-Powered ChatbotProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581252(1-17)Online publication date: 19-Apr-2023
    • Show More Cited By

    View Options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Get Access

    Login options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media