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    <title>Data Science on E=mc&lt;sup&gt;2&lt;/sup&gt;</title>
    <link>https://mazziotti.uchicago.edu/emc2/tags/data-science/</link>
    <description>Recent content in Data Science on E=mc&lt;sup&gt;2&lt;/sup&gt;</description>
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      <title>Framing Policy Through Humor: A Computational Analysis of Reddit Memes</title>
      <link>https://mazziotti.uchicago.edu/emc2/years/2026/kesselheim_m/</link>
      <pubDate>Wed, 31 Dec 1969 18:33:46 -0600</pubDate>
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      <description>&lt;p&gt;Internet memes are often viewed as entertainment, but they can also shape political discussion and public opinion. In this project, I examined how political memes on Reddit responded to major political events between 2020 and 2025. Using topic modeling, time-series analysis, and machine learning techniques, I identified recurring themes in thousands of memes and analyzed how their frequency changed in response to real-world events. By studying humor as a form of political communication, this work explores how online communities frame policy issues and influence public engagement through satire and shared narratives.&lt;/p&gt;</description>
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      <title>Machine Learning to Predict Stroke Outcomes</title>
      <link>https://mazziotti.uchicago.edu/emc2/years/2025/cummins_j/</link>
      <pubDate>Wed, 31 Dec 1969 18:33:45 -0600</pubDate>
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      <description>&lt;p&gt;Stroke remains one of the leading causes of death and long-term disability worldwide, making accurate prediction of patient outcomes an important medical challenge. In this project, I applied machine learning techniques to clinical datasets to identify patterns associated with stroke prognosis and patient survival. By developing predictive models and evaluating their performance across diverse populations, I explored how data science can support clinical decision-making and help physicians provide more personalized care for patients at risk.&lt;/p&gt;</description>
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