minOffense: Inter-Agreement Hate Terms for Stable Rules, Concepts,
  Transitivities, and Lattices
Animesh Chaturvedi Rajesh Sharma
Hate speech classification has become an important problem due to the spread
of hate speech on social media platforms. For a given set of Hate Terms lists
(HTs-lists) and Hate Speech data (HS-data), it is challenging to understand
which hate term contributes the most for hate speech classification. This paper
contributes two approaches to quantitatively measure and qualitatively
visualise the relationship between co-occurring Hate Terms (HTs). Firstly, we
propose an approach for the classification of hate-speech by producing a Severe
Hate Terms list (Severe HTs-list) from existing HTs-lists. To achieve our goal,
we proposed three metrics (Hatefulness, Relativeness, and Offensiveness) to
measure the severity of HTs. These metrics assist to create an Inter-agreement
HTs-list, which explains the contribution of an individual hate term toward
hate speech classification. Then, we used the Offensiveness metric values of
HTs above a proposed threshold minimum Offense (minOffense) to generate a new
Severe HTs-list. To evaluate our approach, we used three hate speech datasets
and six hate terms lists. Our approach shown an improvement from 0.845 to 0.923
(best) as compared to the baseline. Secondly, we also proposed Stable Hate Rule
(SHR) mining to provide ordered co-occurrence of various HTs with minimum
Stability (minStab). The SHR mining detects frequently co-occurring HTs to form
Stable Hate Rules and Concepts. These rules and concepts are used to visualise
the graphs of Transitivities and Lattices formed by HTs.