Overview of the HASOC Subtrack at FIRE 2023: Identification of Tokens
  Contributing to Explicit Hate in English by Span Detection
Sarah Masud Mohammad Aflah Khan Md. Shad Akhtar Tanmoy Chakraborty
As hate speech continues to proliferate on the web, it is becoming
increasingly important to develop computational methods to mitigate it.
Reactively, using black-box models to identify hateful content can perplex
users as to why their posts were automatically flagged as hateful. On the other
hand, proactive mitigation can be achieved by suggesting rephrasing before a
post is made public. However, both mitigation techniques require information
about which part of a post contains the hateful aspect, i.e., what spans within
a text are responsible for conveying hate. Better detection of such spans can
significantly reduce explicitly hateful content on the web. To further
contribute to this research area, we organized HateNorm at HASOC-FIRE 2023,
focusing on explicit span detection in English Tweets. A total of 12 teams
participated in the competition, with the highest macro-F1 observed at 0.58.