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.

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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
Controls
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article

COVID Infodemic Advisor (CIA): An Online Recommender For Content Consumers & Providers

Seth Adjei, Chase Maschinot, Ankur Chattopadhyay
1 citations
Sentiment Analysis and Opinion Mining
2.48
article

COVID Infodemic Advisor (CIA): An Online Recommender For Content Consumers & Providers

Seth Adjei, Chase Maschinot, Ankur Chattopadhyay
article en
1 citations

Abstract

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.

Northern Kentucky University (US)
Good health and well-being
Openalex Percentile: Top 10%
Sentiment Analysis and Opinion Mining
2.48
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