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      <title>Why social science needs to embrace machine learning</title>
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      <pubDate>Fri, 06 Jun 2025 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;&lt;img alt=&#34;machine_learning&#34; loading=&#34;lazy&#34; src=&#34;https://wolflytics.com/images/soml.jpg&#34;&gt;&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Quick note&lt;/strong&gt;: This is just the first post in a series; think of it as a short introduction.&lt;/p&gt;
&lt;p&gt;I&amp;rsquo;m a social scientist, and unlike many in my field, I genuinely enjoy math and statistics. My friend Petar jokes that statistics isn&amp;rsquo;t &lt;em&gt;real&lt;/em&gt; math, but I&amp;rsquo;ll leave that debate for another time.&lt;/p&gt;
&lt;p&gt;What matters is that social science has long struggled to explain society&amp;rsquo;s complexity. We rely heavily on methods like surveys, interviews, and content analysis. These are useful but often fall short when predicting or uncovering &lt;em&gt;causal relationships&lt;/em&gt;. Society is messy, and humans are unpredictable.&lt;/p&gt;</description>
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      <title>Serbian Parliamentary NLP Analysis</title>
      <link>https://wolflytics.com/projects/serbian-parliamentary-nlp-analysis/</link>
      <pubDate>Wed, 14 May 2025 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;Political discourse shapes policy, and policy shapes lives. But how do politicians actually speak? What topics dominate their agendas? And do their words reflect the concerns of everyday citizens? Using NLP, we can answer these questions with data not assumptions&lt;/p&gt;
&lt;h2 id=&#34;introduction&#34;&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Natural Language Processing (NLP) has become a central pillar of AI, especially with the rise of Large Language Models (LLMs). At its core, an LLM is just NLP applied at scale—processing massive amounts of text to generate human-like responses.&lt;/p&gt;</description>
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