minOffense：用于稳定规则、概念、传递性与格的跨共识仇恨术语
minOffense: Inter-Agreement Hate Terms for Stable Rules, Concepts, Transitivities, and Lattices
AI总结：
本文提出基于Hatefulness、Relativeness和Offensiveness指标及minOffense阈值构建严重仇恨术语列表，并利用稳定仇恨规则挖掘共现关系，以提升仇恨言论分类性能并可视化术语间的传递性与格结构。
摘要：
由于仇恨言论在社交媒体平台上的传播，仇恨言论分类已成为一个重要问题。对于给定的一组仇恨术语列表（HTs-lists）和仇恨言论数据（HS-data），理解哪个仇恨术语对仇恨言论分类贡献最大是具有挑战性的。本文提出了两种方法，用于定量测量和定性可视化共现仇恨术语（HTs）之间的关系。首先，我们提出了一种通过从现有HTs-lists中生成严重仇恨术语列表（Severe HTs-list）来进行仇恨言论分类的方法。为实现这一目标，我们提出了三个指标（Hatefulness、Relativeness和Offensiveness）来衡量HTs的严重程度。这些指标有助于创建一个跨共识HTs-list，该列表解释了单个仇恨术语对仇恨言论分类的贡献。然后，我们使用高于所提出阈值最小冒犯度（minOffense）的HTs的Offensiveness指标值来生成新的Severe HTs-list。为评估我们的方法，我们使用了三个仇恨言论数据集和六个仇恨术语列表。与基线相比，我们的方法显示从0.845提升到0.923（最佳）。其次，我们还提出了稳定仇恨规则（SHR）挖掘，以提供具有最小稳定性（minStab）的各种HTs的有序共现。SHR挖掘检测频繁共现的HTs，以形成稳定仇恨规则和概念。这些规则和概念用于可视化由HTs形成的传递性和格图。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
