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.
Supplemental Material
Available for Download
Supplementary Material for Powering an AI Chatbot with Expert Sourcing to Support Credible Health Information Access
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google Scholar
- Senator Lamar Alexander. 2020. Preparing for the Next Pandemic. https://www.alexander.senate.gov. [Online; accessed 16-June-2020].Google Scholar
- 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).Google Scholar
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.pdfGoogle Scholar
- 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).Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Chatfuel. 2020.. [Online; accessed June-2020].Google Scholar
- 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.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google Scholar
- Dialogflow. 2020.. [Online; accessed June-2020].Google Scholar
- James Price Dillard, Ruobing Li, and Chun Yang. 2020. Fear of Zika: Information seeking as cause and consequence. Health Communication(2020), 1–11.Google Scholar
- 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.Google ScholarDigital Library
- 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.Google Scholar
- Julian J Faraway. 2016. Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. CRC press.Google Scholar
- Asbjørn Følstad and Petter Bae Brandtzæg. 2017. Chatbots and the new world of HCI. interactions 24, 4 (2017), 38–42.Google Scholar
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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).Google Scholar
- 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.Google ScholarDigital Library
- Christine Hagar. 2015. Crisis informatics. In Encyclopedia of Information Science and Technology, Third Edition. IGI Global, 1350–1358.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google Scholar
- Juji. 2020. Juji document for chatbot designers. https://docs.juji.io/. [Online; accessed 14-June-2020].Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google Scholar
- 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.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google ScholarDigital Library
- Wenhong Luo and Mohammad Najdawi. 2004. Trust-building measures: a review of consumer health portals. Commun. ACM 47, 1 (2004), 108–113.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarDigital Library
- Ida Norheim-Hagtun and Patrick Meier. 2010. Crowdsourcing for crisis mapping in Haiti. Innovations: Technology, Governance, Globalization 5, 4(2010), 81–89.Google Scholar
- 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.Google ScholarCross Ref
- 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).Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Archita Pathak and Rohini Srihari. 2019. BREAKING! Presenting Fake News Corpus for Automated Fact Checking. In ACL (Student Research Workshop).Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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).Google Scholar
- 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.Google Scholar
- 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.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google Scholar
- 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.Google ScholarDigital Library
- Briony Swire-Thompson and David Lazer. 2020. Public health and online misinformation: challenges and recommendations. Annual Review of Public Health 41 (2020), 433–451.Google ScholarCross Ref
- Leila Tavakoli. 2020. Generating Clarifying Questions in Conversational Search Systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 3253–3256.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google ScholarDigital Library
- 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).Google Scholar
Index Terms
Powering an AI Chatbot with Expert Sourcing to Support Credible Health Information Access
Recommendations
Informational social support and online health information seeking
We explored processes involving social support, web use, and healthy eating.Informational support positively predicted web use.Informational support positively predicted favorable perceptions of web resources.Web use mediated the relationship between ...
Shifting Trust: Examining How Trust and Distrust Emerge, Transform, and Collapse in COVID-19 Information Seeking
CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing SystemsDuring crises like COVID-19, individuals are inundated with conflicting and time-sensitive information that drives a need for rapid assessment of the trustworthiness and reliability of information sources and platforms. This parallels evolutions in ...
Aging Well with Health Information: Examining Health Literacy and Information Seeking Behavior Using a National Survey Dataset
Digital Libraries at Times of Massive Societal TransitionAbstractHealth literacy is critical in disease prevention particularly in the older population. This secondary data analysis of a national survey is to determine the levels of health literacy, and to investigate how it links to health information seeking ...
Comments