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SUMMARY:Revisiting and re-evaluating rumour stance classification - Caroli
 na Scarton (University of Sheffield)
DTSTART:20210122T120000Z
DTEND:20210122T130000Z
UID:TALK156103@talks.cam.ac.uk
CONTACT:James Thorne
DESCRIPTION:Join Zoom Meeting\nhttps://cl-cam-ac-uk.zoom.us/j/91334942020?
 pwd=eDJXd09wdW1FUDMvWFBoenovMDdNUT09\n\nMeeting ID: 913 3494 2020\nPasscod
 e: 565263\n\nIn this talk I am going to present our recent work on rumour 
 stance classification. This task consists of automatically classifying the
  stance of replies to a given rumour\, which has been proven to help in th
 e verification of the rumour itself. Traditionally framed as a four-class 
 classification problem (support\, deny\, query\, comment)\, the available 
 datasets are highly imbalanced\, with a large majority of examples in the 
 comment class. However\, the two most important classes for this task are 
 deny and support\, since they express the crowd perception towards the rum
 our. Aiming to improve the performance of classification models in these t
 wo most important classes\, we experiment with traditional feature-based a
 s well as BERT-based approaches\, using well-known techniques for dealing 
 with data imbalance problems. In addition\, we re-evaluate two widely know
 n shared tasks on rumour stance classification\, highlighting that reliabl
 e and detailed evaluation needs to be performed in order to select systems
  for this task.
LOCATION:Virtual (Zoom)
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