COVID Infodemic Advisor (CIA): An Online Recommender For Content Consumers & Providers
COVID related false claims and fake information still remain a serious challenge to societal stakeholders, and there is a need from the online health information consumer side to validate COVID health contents and alert online users about unreliable data. On the other hand, there is also a need from the content provider side to certify the provider hosted online health info contents, so that the providers can have the hosted contents accredited in terms of reliability for earning user trust and for building credibility. Existing literature shows that there have been research & development initiatives for supporting online health content consumers with false information identification. However, none of these previous works have looked into supporting the content provider need of having an avenue to get provider hosted health info content certified in order to boost user credibility. In this paper, we present COVID Infodemic Advisor (CIA), a unique online recommender tool-based model, that can vet online COVID health information to serve both content consumers and content providers, as an info validator, which leads to evidence of verification for establishing trustworthiness. We also discuss how we have implemented our CIA model as a nifty automated content reviewer driven web extension and show how it can work as an online recommender for content consumers, as well as a trust badge utility for serving content providers. To our knowledge, this work is a first-of-its-kind initiative to create an online vetting resource, that is intended to build content provider credentials as a trust badge, in addition to benefiting content consumers as an online advisor.
Authors
- Seth Adjei (ORCID: https://orcid.org/0000-0002-2445-597X)
- Chase Maschinot
- Ankur Chattopadhyay (ORCID: https://orcid.org/0009-0000-8603-1200)
Institutions
- Northern Kentucky University (US)
Publication Details
- Published
- 2025-04-24
- DOI
- https://doi.org/10.1109/isdfs65363.2025.11011915
- Citations
- 1
- Primary Topic
- Sentiment Analysis and Opinion Mining
- Type
- article
- Field-Weighted Citation Impact
- 2.48