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<oembed><version>1.0</version><provider_name>Department of Computer Science</provider_name><provider_url>https://www.cs.jhu.edu</provider_url><title>Computer Science Student Defense:: Adrian Benton, Johns Hopkins University &#x2013; &#x201C;Learning Representations of Social Media Users&#x201D; - Department of Computer Science</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content"&gt;&lt;a href="https://www.cs.jhu.edu/event/computer-science-student-defense-adrian-benton-johns-hopkins-university-learning-representations-of-social-media-users/"&gt;Computer Science Student Defense:: Adrian Benton, Johns Hopkins University &#x2013; &#x201C;Learning Representations of Social Media Users&#x201D;&lt;/a&gt;&lt;/blockquote&gt;
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&lt;/script&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.cs.jhu.edu/event/computer-science-student-defense-adrian-benton-johns-hopkins-university-learning-representations-of-social-media-users/embed/" width="600" height="338" title="&#x201C;Computer Science Student Defense:: Adrian Benton, Johns Hopkins University &#x2013; &#x201C;Learning Representations of Social Media Users&#x201D;&#x201D; &#x2014; Department of Computer Science" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;</html><description>LocationMalone 228AbstractUsers on social media platforms routinely interact by posting text messages, sharing images and videos, and establishing connections with other users through friending. Learning latent user representations from these observations is an important problem for marketers, public policy experts, social scientists, and computer scientists.In this thesis, we show how user representations can be learned&hellip;</description></oembed>
