探索攻击性与仇恨言论的边界与强度：揭示社交媒体话语的复杂光谱
Exploring Boundaries and Intensities in Offensive and Hate Speech: Unveiling the Complex Spectrum of Social Media Discourse
AI总结：
本研究构建阿姆哈拉语仇恨言论基准数据集，通过分类与回归模型揭示攻击性与仇恨强度的连续光谱，Afro-XLMR-large模型表现最佳。
摘要：
数字媒体的普及和不断演变的社会政治动态显著加剧了仇恨内容的传播。现有研究主要侧重于将文本分类为二元类别，往往忽视了文本中固有的攻击性和仇恨性的连续光谱。在本研究中，我们提出了一个广泛的阿姆哈拉语基准数据集，包含8,258条推文，并为三个不同的任务进行了标注：类别分类、仇恨目标识别以及攻击性和仇恨强度的评级。我们的研究强调，相当多数的推文属于较低攻击性和较低仇恨强度级别，这凸显了利益相关者进行早期干预的必要性。在我们的数据集中，种族和政治仇恨目标的普遍存在及其显著重叠，强调了埃塞俄比亚社会政治格局中复杂的关系。我们构建了分类和回归模型，并研究了这些模型在处理这些任务中的有效性。我们的结果表明，仇恨和攻击性言论不能通过简单的二元分类来解决，而是表现为连续范围内的变量。Afro-XLMR-large模型表现出最佳性能，在类别、目标和回归任务中分别获得了75.30%、70.59%和29.42%的F1分数。Afro-XLMR-large模型的80.22%相关系数表明了强烈的一致性。
英文摘要：
The prevalence of digital media and evolving sociopolitical dynamics have significantly amplified the dissemination of hateful content. Existing studies mainly focus on classifying texts into binary categories, often overlooking the continuous spectrum of offensiveness and hatefulness inherent in the text. In this research, we present an extensive benchmark dataset for Amharic, comprising 8,258 tweets annotated for three distinct tasks: category classification, identification of hate targets, and rating offensiveness and hatefulness intensities. Our study highlights that a considerable majority of tweets belong to the less offensive and less hate intensity levels, underscoring the need for early interventions by stakeholders. The prevalence of ethnic and political hatred targets, with significant overlaps in our dataset, emphasizes the complex relationships within Ethiopia's sociopolitical landscape. We build classification and regression models and investigate the efficacy of models in handling these tasks. Our results reveal that hate and offensive speech can not be addressed by a simplistic binary classification, instead manifesting as variables across a continuous range of values. The Afro-XLMR-large model exhibits the best performances achieving F1-scores of 75.30%, 70.59%, and 29.42% for the category, target, and regression tasks, respectively. The 80.22% correlation coefficient of the Afro-XLMR-large model indicates strong alignments.
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。
