  Offensive or antagonistic language targeted at individuals and social groups
based on their personal characteristics (also known as cyber hate speech or
cyberhate) has been frequently posted and widely circulated viathe World Wide
Web. This can be considered as a key risk factor for individual and societal
tension linked toregional instability. Automated Web-based cyberhate detection
is important for observing and understandingcommunity and regional societal
tension - especially in online social networks where posts can be rapidlyand
widely viewed and disseminated. While previous work has involved using
lexicons, bags-of-words orprobabilistic language parsing approaches, they often
suffer from a similar issue which is that cyberhate can besubtle and indirect -
thus depending on the occurrence of individual words or phrases can lead to a
significantnumber of false negatives, providing inaccurate representation of
the trends in cyberhate. This problemmotivated us to challenge thinking around
the representation of subtle language use, such as references toperceived
threats from "the other" including immigration or job prosperity in a hateful
context. We propose anovel framework that utilises language use around the
concept of "othering" and intergroup threat theory toidentify these subtleties
and we implement a novel classification method using embedding learning to
computesemantic distances between parts of speech considered to be part of an
"othering" narrative. To validate ourapproach we conduct several experiments on
different types of cyberhate, namely religion, disability, race andsexual
orientation, with F-measure scores for classifying hateful instances obtained
through applying ourmodel of 0.93, 0.86, 0.97 and 0.98 respectively, providing
a significant improvement in classifier accuracy overthe state-of-the-art
