BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Computer Science - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Computer Science
X-ORIGINAL-URL:https://www.cs.jhu.edu
X-WR-CALDESC:Events for Department of Computer Science
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20170312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20171105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20180311T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20181104T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20190310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20191103T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180907T100000
DTEND;TZID=America/New_York:20180907T120000
DTSTAMP:20260422T144810
CREATED:20210629T210713Z
LAST-MODIFIED:20210629T210713Z
UID:1962095-1536314400-1536321600@www.cs.jhu.edu
SUMMARY:Computer Science Student Defense:: Adrian Benton\, Johns Hopkins University – “Learning Representations of Social Media Users”
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 from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis\, a set of multiview learning objectives\, to learn these representations\, and evaluate them at three tasks: predicting future hashtag mentions\, friending behavior\, and demographic features. We then show how user features can improve three additional downstream tasks: improving topic model fit\, mental health status classification\, and message-level stance classification.BioAdrian Benton received the B.A. degree in Linguistics from the University of Pennsylvania in 2008. He received the M.S. degree in Computer Science from the University of Pennsylvania in 2012\, and enrolled in the Computer Science Ph.D. program at Johns Hopkins University in 2013.  He is advised by Dr. Mark Dredze. His research centers around applying machine learning techniques to analyze social media data.
URL:https://www.cs.jhu.edu/event/computer-science-student-defense-adrian-benton-johns-hopkins-university-learning-representations-of-social-media-users/
END:VEVENT
END:VCALENDAR