A Contribution to COVID-19 Prevention through Crowd Collaboration using Conversational AI & Social Platforms

• URL: http://arxiv.org/abs/2106.11023v1
• Date: Wed, 9 Jun 2021 04:46:42 GMT
• Title: A Contribution to COVID-19 Prevention through Crowd Collaboration using Conversational AI & Social Platforms
• Authors: Jawad Haqbeen, Takayuki Ito, Sofia Sahab, Rafik Hadfi, Shun Okuhara, Nasim Saba, Murataza Hofaini, Umar Baregzai
• Abstract summary: We conducted a large-scale digital social experiment using conversational AI and social platforms from an info-epidemiology and an infoveillance perspective. This paper shows that deciding a prevention measure that maximizes the probability of finding the ground truth is intrinsically difficult without utilizing the support of an AI-enabled discussion systems.
• Score: 0.9674544640949528
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: COVID-19 Prevention, which combines the soft approaches and best practices for public health safety, is the only recommended solution from the health science and management society side considering the pandemic era. In an attempt to evaluate the validity of such claims in a conflict and COVID-19-affected country like Afghanistan, we conducted a large-scale digital social experiment using conversational AI and social platforms from an info-epidemiology and an infoveillance perspective. This served as a means to uncover an underling truth, give large-scale facilitation support, extend the soft impact of discussion to multiple sites, collect, diverge, converge and evaluate a large amount of opinions and concerns from health experts, patients and local people, deliberate on the data collected and explore collective prevention approaches of COVID-19. Finally, this paper shows that deciding a prevention measure that maximizes the probability of finding the ground truth is intrinsically difficult without utilizing the support of an AI-enabled discussion systems.

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