论文标题
“目标或不定位”：使用分类器集合对滥用文本进行识别和分析
"To Target or Not to Target": Identification and Analysis of Abusive Text Using Ensemble of Classifiers
论文作者
论文摘要
由于对社交媒体平台上的虐待和仇恨行为的关注不断增加，我们提出了一种合奏学习方法，以识别和分析此类内容的语言特性。我们堆叠的合奏包括三种机器学习模型，它们捕获语言的不同方面，并提供有关不适当语言的多样和连贯的见解。所提出的方法为Twitter滥用行为数据集（Founta et al.2018）上现有的最新结果提供了可比的结果，而无需使用任何与用户或网络相关的信息；仅依靠文本属性。我们认为，提出的见解和讨论当前方法的缺点将突出未来研究的潜在方向。
With rising concern around abusive and hateful behavior on social media platforms, we present an ensemble learning method to identify and analyze the linguistic properties of such content. Our stacked ensemble comprises of three machine learning models that capture different aspects of language and provide diverse and coherent insights about inappropriate language. The proposed approach provides comparable results to the existing state-of-the-art on the Twitter Abusive Behavior dataset (Founta et al. 2018) without using any user or network-related information; solely relying on textual properties. We believe that the presented insights and discussion of shortcomings of current approaches will highlight potential directions for future research.
