<div dir="ltr">FYI<br clear="all"><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div><br></div><div>Best,</div><div dir="ltr">Xiaobai Liu, PhD<div>Associate Professor of Computer Science</div><div>Director of Machine Vision and Perception Lab<br></div><div>San Diego State University</div><div>CA, 92182</div><div><br></div><div><br></div><div><br></div></div></div></div></div></div></div></div></div></div><br><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">---------- Forwarded message ---------<br>From: <strong class="gmail_sendername" dir="auto">Xiaobai Liu</strong> <span dir="auto"><<a href="mailto:xiaobai.liu@sdsu.edu">xiaobai.liu@sdsu.edu</a>></span><br>Date: Thu, Jan 23, 2020 at 11:27 PM<br>Subject: AI Seminar Jan 28, Tuesday, 10:30-11:30am, GMCS405<br>To: Computer Science Faculty <<a href="mailto:csfac@sdsu.edu">csfac@sdsu.edu</a>><br></div><br><br><div dir="ltr">Dear All: <div>Happy New Semester! </div><div><br></div><div>Please check this seminar for our first event this spring. Dr. Shaozeng from Oregon State University will present his recent research outcomes. </div><div><br></div><div>Please let me know if you would like to speak with him in person. </div><div><br></div><div>

<p class="MsoNormal" style="margin:0in 0in 0.0001pt;font-size:12pt;font-family:"Times New Roman""><a name="m_2695100183649072661_OLE_LINK158" style="font-size:12pt;text-align:center;text-indent:0.5in"><span style="font-size:15pt;font-family:FZShuTi"> </span></a></p>

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<p class="MsoNormal" style="line-height:16pt;margin:0in 0in 0.0001pt;font-size:12pt;font-family:"Times New Roman""><a name="m_2695100183649072661_OLE_LINK4"></a><a name="m_2695100183649072661_OLE_LINK3"><b><span style="font-size:15pt;font-family:Arial;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">Contextual
interpretation of social media users’ interaction with content recommendation
algorithm in the “post-truth” era today</span></b></a></p>

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<p class="MsoNormal" style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial;margin:0in 0in 0.0001pt;font-size:12pt;font-family:"Times New Roman""><b>Speaker    </b>Dr. Shaozeng Zhang</p>

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Professor, department of anthropology, </p>

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University</p>

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<p class="MsoNormal" style="line-height:16pt;margin:0in 0in 0.0001pt;font-size:12pt;font-family:"Times New Roman"">Dr. Shaozeng Zhang has been serving as an Assistant Professor in the
department of anthropology at Oregon State University (OSU) since 2017. Before
joining OSU, he had also worked at Colorado State University. He received his
PhD degree in cultural anthropology from the University of California, Irvine
in 2014. His research has been focusing on the interdisciplinary field of
Science, Technology and Society (STS), with particular interests in social
trajectories of environmental sciences in policymaking and ,since more recently,
in human interface with digital technologies.</p>

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<p class="MsoNormal" style="line-height:16pt;margin:0in 0in 0.0001pt;font-size:12pt;font-family:"Times New Roman"">This study examines an online protest
movement on location-based social media in order to develop contextual
interpretation of user-generated big data and to challenge the ongoing debates
on “post-truth”. In 2016, in support of the local protests against a crude oil
pipeline passing through the region, social media users all over the world
remotely checked in to and wrote location-based reviews of the Standing Rock
Indian Reservation, North Dakota using a technique that can be called “location
spoofing.” This collective action thus generated a massive volume of “fake”
locational information. This study develops an anthropological approach to
user-generated big data as digital traces of human activities in the broader
social-technological network of the involved human and non-human actors. This
study reveals that the online protesters’ use of location-based features and
content recommendation algorithm challenged not only the political and
technological authorities but also, at a more profound level, the established
ways<span style="font-family:Arial;color:black">
</span>for determining what is true and who gets to decide what is true for what
purpose. This unique case of decentralized data generation and dissemination
demonstrates an ongoing reconfiguration of the previously established regime of
truth that was monopolized by scientists or top politicians. The contextual
approach to data interpretation is a pioneering effort to update our
epistemological assumptions about truth and our research methodology in data
environments today. It calls on scholars across disciplines to envision an
epistemological shift in our continuous pursuit of knowledge and truth in the
so-called “post-truth” era today.<span style="font-family:Arial;color:black"></span></p>

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<a name="m_2695100183649072661_OLE_LINK12"></a><a name="m_2695100183649072661_OLE_LINK11">Host:   Dr. Xiaobai Liu</a></span></p>

<p class="MsoNormal" style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial;margin:0in 0in 0.0001pt;font-size:12pt;font-family:"Times New Roman"">Student Organizer:    Patrick
Perrine</p>



<p class="MsoNormal" style="margin:0in 24pt 0.0001pt 0in;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial;font-size:12pt;font-family:"Times New Roman"">Web:    sdsuai.home.blog</p></div><div><div><div dir="ltr" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div><br></div><div>Best,</div><div dir="ltr">Xiaobai Liu, PhD<div>Associate Professor of Computer Science</div><div>Director of Machine Vision and Perception Lab<br></div><div>San Diego State University</div><div>CA, 92182</div><div><br></div><div><br></div><div><br></div></div></div></div></div></div></div></div></div></div></div></div>
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