BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Mining the Social Web: A series of statistical NLP case studies - 
 Vasileios Lampos
DTSTART:20141205T120000Z
DTEND:20141205T130000Z
UID:TALK54049@talks.cam.ac.uk
CONTACT:Tamara Polajnar
DESCRIPTION:[ "Slides":http://www.lampos.net/sites/default/files/slides/La
 mpos2014SNLP_on_Twitter.pdf ]\n\n*Abstract*\n\nOver the past few years use
 r-generated content has been the centre of various research efforts in the
  domain of statistical natural language processing. In particular\, the op
 en nature of the microblogging platform of Twitter provided the opportunit
 y for various appealing ideas to be evaluated. Based on the hypothesis tha
 t this online stream of content should represent at least a biased fractio
 n of real-world situations or opinions\, we have proposed core algorithms 
 for nowcasting the rate of an infectious disease\, such as influenza\, or 
 even a natural phenomenon like rainfall rates [1\,2]. A simplified emotion
  analysis on a longitudinal set of tweets revealed interesting patterns\, 
 including signs of rising anger and fear before the UK riots in August\, 2
 011 [3]. By extending linear text regression approaches to embed user rele
 vance\, we proposed a family of bilinear regularised regression models\, w
 hich found application in the approximation of voting intention trends [4]
 . Finally\, we attempted to reverse the previous modelling principle to lo
 ok into how various user attributes or behaviours may influence a generic 
 notion of user-impact [5].\n\n*References*\n# Lampos and Cristianini. Trac
 king the flu pandemic by monitoring the Social Web\, Cognitive Information
  Processing '10.\n# Lampos and Cristianini. Nowcasting Events from the Soc
 ial Web with Statistical Learning\, ACM TIST (2012).\n# Lansdall-Welfare\,
  Lampos and Cristianini. Effects of the Recession on Public Mood in the UK
 \, WWW '12.\n# Lampos\, Preotiuc-Pietro and Cohn. A user-centric model of 
 voting intention from Social Media\, ACL '13.\n# Lampos\, Aletras\, Preoti
 uc-Pietro and Cohn. Predicting and Characterising User Impact on Twitter\,
  EACL '14.\n
LOCATION:FW26\, Computer Laboratory
END:VEVENT
END:VCALENDAR