English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

As hate speech continues to proliferate on the web, it is becomingincreasingly important to develop computational methods to mitigate it.Reactively, using black-box models to identify hateful content can perplexusers as to why their posts were automatically flagged as hateful. On the otherhand, proactive mitigation can be achieved by suggesting rephrasing before apost is made public. However, both mitigation techniques require informationabout which part of a post contains the hateful aspect, i.e., what spans withina text are responsible for conveying hate. Better detection of such spans cansignificantly reduce explicitly hateful content on the web. To furthercontribute to this research area, we organized HateNorm at HASOC-FIRE 2023,focusing on explicit span detection in English Tweets. A total of 12 teamsparticipated in the competition, with the highest macro-F1 observed at 0.58.

Overview of the HASOC Subtrack at FIRE 2023: Identification of Tokens Contributing to Explicit Hate in English by Span Detection