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Assessing Vaccine-Related Content for Journalistic Quality: A large-scale dataset and article repository

<p>This dataset was produced through a collaboration with the NSF-funded <a href="https://artt.cs.washington.edu/">ARTT project</a> (led by Hacks/Hackers) and <a href="https://overtone.ai/">Overtone</a>. It consists of 1,000 vaccine-related articles, pulled from a wide variety of news media sources, with associated scores based on their journalistic quality. The scores were provided through Overtone&rsquo;s algorithm, and range from one (low-quality or low informational value add) to five (high-quality or high informational value add). Articles were sourced from&nbsp;traditional journalism outlets (news and news-leaning websites), as well as non-journalistic sources of vaccine information, such as governmental websites, healthcare and NGO websites, and medical journals.&nbsp;Given the algorithm&rsquo;s focus on editorial content, as opposed to other metrics such as author, outlet, or engagement, analyzing a diverse set of article types allowed the research team&nbsp;to examine how different styles of vaccine-related content measured against traditional journalistic quality standards.<strong>&nbsp;</strong>Therefore, this dataset provides a unique insight into the spectrum of vaccine reporting, and serves as a contribution to the field of&nbsp;automated quality assessment.&nbsp;</p> <p><strong>About ARTT:</strong> The Analysis and Response Toolkit for Trust (ARTT) project is focused on helping people engage in trust-building ways when discussing vaccine efficacy and other topics online.&nbsp;</p> <p><strong>About Overtone:</strong>&nbsp;Overtone has built a Natural Language Processing algorithm that finds and sorts online content by its intrinsic qualities, rather than clicks or shares. Their AI assesses texts for journalistic signals that demonstrate human effort.</p> <p>For any questions about this dataset, please contact artt@hackshackers.com.</p>