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UID:calendar.360481.field_event_date.0@www.wright.edu
DTSTAMP:20260220T000850Z
CREATED:20190610T123815Z
DESCRIPTION:Ph.D. Committee:  Drs. Amit Sheth\, Advisor\, Keke Chen\, Krish
 naprasad Thirunarayan\, Valerie Shalin (Department of Psychology) and Bran
 don Minnery (Kairos Research) ABSTRACTThe wisdom of the crowd is a well-kn
 own example of collective intelligence wherein an aggregated judgment of a
  group of individuals is superior to that of an individual. The aggregated
  judgment is surprisingly accurate to predict the outcome of a range of ev
 ents from geopolitical forecasting to stock price index. Recent research s
 tudies have shown that participants’ previous performance data contributes
  to the identification of a subset of participants that can collectively p
 redict an accurate outcome. In the absence of such performance data\, rese
 archers have explored the role of human-perceived diversity to assemble an
  intelligent crowd. The online social networks are becoming increasingly p
 opular in sharing and seeking domain-specific knowledge. Tapping the crowd
  wisdom on online social networks can help prediction for several real-wor
 ld tasks. Independent and contextually diverse crowd selection using socia
 l media data imposes unique challenges such as\,Complementing short and po
 tentially noisy social media data with domain specific knowledge.Lack of l
 abeled data in evaluating closely connected social media participants.Comb
 ining social media text\, indicating a diverse perspective\, and network f
 eatures\, indicating potential influence\, in diverse crowd selection.Inte
 rpretable diversity measure to understand the type of diversity that can e
 nhance the performance of a crowd.This dissertation first provides several
  data-driven measures from social-media data and shows that participant di
 versity can be inferred from social media data and that it can benefit per
 formance in the real world prediction tasks. Domain-specific knowledge gra
 phs provide the foundational basis to evaluate and drive contextually dive
 rse crowd selection and complementing short social media text. Novel group
  detection and crowd selection algorithms incorporating text\, network\, a
 nd knowledge-graph can automatically select diverse crowd and also provide
  recognizable diversity interpretation. It is shown that such a diverse cr
 owd can accurately predict an outcome of real-world events. These results 
 have implications for numerous domains that utilize aggregated judgments -
  from consumer reviews to econometrics\, to geopolitical forecasting and i
 ntelligence analysis.
DTSTART;TZID=America/New_York:20190613T093000
DTEND;TZID=America/New_York:20190613T113000
LAST-MODIFIED:20190610T133332Z
LOCATION:366 Joshi Research Center
SUMMARY:Ph.D. Dissertation Defense “Data-driven and Knowledge-driven strate
 gies for Realizing Crowd Wisdom on Social Media” By Shreyansh Bhatt
URL;TYPE=URI:/events/phd-dissertation-defense-%E2%80%
 9Cdata-driven-knowledge-driven-strategies-realizing-crowd-wisdom
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