使用迁移学习的伊斯兰恐惧症推文检测

Mohd. Belal, Ghufran Ullah, Abdullah Ahmad Khan
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引用次数: 1

摘要

在Twitter等社交媒体平台上,将仇视伊斯兰教的仇恨言论与其他攻击性语言区分开来,是自动检测仇恨言论的一个严重障碍。由于词汇检测方法对包含仇恨言论等特定术语的所有信息进行分类,因此之前使用监督学习的工作未能区分这些类别。由于自然语言结构的难度,这项任务很复杂。我们研究了一种使用通用语言模型微调(ULMFIT)的迁移学习方法,这是一种可以应用于分类任务的有效方法。我们的方法给出了超过80%的准确率,并且由此形成的混淆矩阵能够成功地将这些数据集按比例分类到每个块中。深度学习在文本分类中的应用尚未得到充分利用。这种方法将有助于解决伊斯兰恐惧症的蔓延,而在采取行动打击网络仇恨时,这一问题并未被考虑在内
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Islamophobic Tweet Detection using Transfer Learning
Segregating Islamophobic hate speech from other instances of offensive language is a serious hurdle for automatic hate-speech detection on social media platforms such as Twitter. Because lexical detection methods classify all messages containing particular terms like hate speech, previous work using supervised learning has failed to differentiate between these categories. This task is complex due to the level of difficulty in natural language constructs. We have worked on a transfer learning approach using Universal Language Model Fine-tuning (ULMFIT), an efficient method that can be applied to classification tasks. Our method gave more than 80 percent accuracy and the confusion matrix thus formed was successfully able to classify those datasets proportionally into each block. The use of Deep learning in text classification has been underutilized. This method will contribute to solving the spread of Islamophobia which hasn't been taken into consideration when taking action against online hate
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