Democratizing the Development of Chatbots to Improve Public Health: Feasibility Study of COVID-19 Misinformation

Abstract

Background: Chatbots enable users to have humanlike conversations on various topics and can vary widely in complexity and functionality. An area of research priority in chatbots is democratizing chatbots to all, removing barriers to entry, such as financial ones, to help make chatbots a possibility for the wider global population to improve access to information, help reduce the digital divide between nations, and improve areas of public good (eg, health communication). Chatbots in this space may help create the potential for improved health outcomes, potentially alleviating some of the burdens on health care providers and systems to be the sole voices of outreach to public health.

Objective: This study explored the feasibility of developing a chatbot using approaches that are accessible in low- and middle-resource settings, such as using technology that is low cost, can be developed by nonprogrammers, and can be deployed over social media platforms to reach the broadest-possible audience without the need for a specialized technical team.

Methods: This study is presented in 2 parts. First, we detailed the design and development of a chatbot, VWise, including the resources used and development considerations for the conversational model. Next, we conducted a case study of 33 participants who engaged in a pilot with our chatbot. We explored the following 3 research questions: (1) Is it feasible to develop and implement a chatbot addressing a public health issue with only minimal resources? (2) What is the participants' experience with using the chatbot? (3) What kinds of measures of engagement are observed from using the chatbot?

Results: A high level of engagement with the chatbot was demonstrated by the large number of participants who stayed with the conversation to its natural end (n=17, 52%), requested to see the free online resource, selected to view all information about a given concern, and returned to have a dialogue about a second concern (n=12, 36%).

Conclusions: This study explored the feasibility of and the design and development considerations for a chatbot, VWise. Our early findings from this initial pilot suggest that developing a functioning and low-cost chatbot is feasible, even in low-resource environments. Our results show that low-resource environments can enter the health communication chatbot space using readily available human and technical resources. However, despite these early indicators, many limitations exist in this study and further work with a larger sample size and greater diversity of participants is needed. This study represents early work on a chatbot in its virtual infancy. We hope this study will help provide those who feel chatbot access may be out of reach with a useful guide to enter this space, enabling more democratized access to chatbots for all.

Keywords: COVID-19; chatbot; conversational agent; infodemic; misinformation; motivational interviewing; online health information; social media; usability study; vaccine hesitancy; vaccine misinformation.

Figure 1

Example of conversational connections built using the ManyChat user interface.

Figure 2

Persona for VWise.

Figure 3

Examples of user personas guiding our conversation development.

Figure 4

Conversation flow storing the vaccination status for later conversation tracks.

Figure 5

Conversation flow storing participant concerns and launching conversation tracks based on the vaccination status.

Figure 6

Screenshot of conversation personalization in VWise for a participant who is not vaccinated.

Figure 7

Our conversation design implemented in VWise. CUQ: Chatbot Usability Questionnaire; MI: motivational interviewing.

Figure 8

Conversation flow in VWise. CUQ: Chatbot Usability Questionnaire.

Figure 9

Participants’ journey through VWise in the pilot, with attrition rates. CUQ: Chatbot Usability Questionnaire.