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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=health+misinformation">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=health+misinformation]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for health misinformation]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/112/Online-Health-Information"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1193/Real-Time-Detection-of-Online-Health-Misinformation-using-an-Integrated-Knowledgegraph-LLM-Approach"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection"/>
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 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/112/Online-Health-Information">
  <title><![CDATA[Online Health Information]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/112/Online-Health-Information</link>
  <description><![CDATA[The rise of social media platforms as Online Health Information Sources (OHIS) has increased the spread of health misinformation in cyberspace. The rapid dissemination of false or misleading health information, particularly in public health, can have severe consequences. Misinformation not only endangers public health but also poses significant cybersecurity risks, including eroding trust in credible sources, enabling phishing attacks, and heightening the impact of cyber threats during crises...]]></description>
  <dc:date>2024-01-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1193/Real-Time-Detection-of-Online-Health-Misinformation-using-an-Integrated-Knowledgegraph-LLM-Approach">
  <title><![CDATA[Real-Time Detection of Online Health Misinformation using an Integrated Knowledgegraph-LLM Approach]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1193/Real-Time-Detection-of-Online-Health-Misinformation-using-an-Integrated-Knowledgegraph-LLM-Approach</link>
  <description><![CDATA[Winner of Best Student Paper Award 
The dramatic surge of health misinformation on social media platforms poses a significant threat to public health, contributing to hesitancy in vaccines, delayed medical interventions, and the adoption of untested or harmful treatments. We present a novel, hybrid AI-driven framework designed for the real-time detection of health misinformation on social media platforms while prioritizing user privacy. The framework integrates the strengths of Large Langua...]]></description>
  <dc:date>2025-07-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection">
  <title><![CDATA[Evaluating Causal AI Techniques for Health  Misinformation Detection]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection</link>
  <description><![CDATA[Abstract—The proliferation of health misinformation on social media, particularly regarding chronic conditions such as diabetes, hypertension, and obesity, poses significant public health risks. This study evaluates the feasibility of leveraging Natural Language Processing (NLP) techniques for real-time misinformation detection and classification, focusing on Reddit discussions. Using logistic regression as a baseline model, supplemented by Latent Dirichlet Allocation (LDA) for topic modeli...]]></description>
  <dc:date>2025-03-17</dc:date>
 </item>
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