谷歌浏览器插件
订阅小程序
在清言上使用

minOffense: Inter-Agreement Hate Terms for Stable Rules, Concepts, Transitivities, and Lattices

2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)(2022)

引用 1|浏览5
暂无评分
摘要
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.
更多
查看译文
关键词
Harmful Content Online,Hate Speech,Natural Language Processing,Computational Linguistics,Data Analytics
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要