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  <title><![CDATA[PhD Proposal by Shagun Jhaver]]></title>
  <body><![CDATA[<p><strong>Title:&nbsp;</strong>Identifying Opportunities to Improve Content Moderation.</p>

<p>&nbsp;</p>

<p>Shagun Jhaver</p>

<p>Ph.D. student in Computer Science</p>

<p>School of Interactive Computing</p>

<p>College of Computing</p>

<p>Georgia Institute of Technology</p>

<p>&nbsp;</p>

<p><strong>Date:</strong>&nbsp;Monday, March 11, 2019</p>

<p><strong>Time:</strong>&nbsp;3:00-5:00 PM EST</p>

<p><strong>Location:&nbsp;</strong>TSRB 223</p>

<p>&nbsp;</p>

<p>---</p>

<p><strong>Committee:</strong></p>

<p>&nbsp;</p>

<p>Dr. Amy Bruckman (<em>Advisor</em>, School of Interactive Computing, Georgia Institute of Technology),</p>

<p>Dr. Eric Gilbert (<em>Advisor</em>, School of Interactive Computing, Georgia Institute of Technology),</p>

<p>Dr. Neha Kumar (School of Interactive Computing, Georgia Institute of Technology),</p>

<p>Dr. Keith Edwards (School of Interactive Computing, Georgia Institute of Technology),</p>

<p>Dr. Scott Counts (Social Technologies Group, Microsoft Research).</p>

<p>---</p>

<p>&nbsp;</p>

<p><strong>Summary:</strong></p>

<p>&nbsp;</p>

<p>This thesis contributes a nuanced understanding of the challenges inherent in the design and implementation of fair and efficient content moderation systems. Using large-scale data analyses, participant observations, survey data and in-depth qualitative interviews, this research describes the perspectives and practices of different stakeholders - users who suffer online harassment, individuals whose posts get removed, people who rely on blocking tools to censor others, and community managers who volunteer time to regulate content. This work provides theoretical and practical guidelines for moderating against online harassment without impinging on free speech, designing solutions that incorporate the needs of different user groups, and adopting automated tools while maintaining the ability to provide explanations and remaining sensitive to localized contexts for speech.</p>

<p>&nbsp;</p>
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