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Hate Speech Detection in Roman Urdu
ACM Transactions on Asian and Low-Resource Language Information Processing ( IF 2 ) Pub Date : 2021-03-09 , DOI: 10.1145/3414524
Muhammad Moin Khan 1 , Khurram Shahzad 1 , Muhammad Kamran Malik 1
Affiliation  

Hate speech is a specific type of controversial content that is widely legislated as a crime that must be identified and blocked. However, due to the sheer volume and velocity of the Twitter data stream, hate speech detection cannot be performed manually. To address this issue, several studies have been conducted for hate speech detection in European languages, whereas little attention has been paid to low-resource South Asian languages, making the social media vulnerable for millions of users. In particular, to the best of our knowledge, no study has been conducted for hate speech detection in Roman Urdu text, which is widely used in the sub-continent. In this study, we have scrapped more than 90,000 tweets and manually parsed them to identify 5,000 Roman Urdu tweets. Subsequently, we have employed an iterative approach to develop guidelines and used them for generating the Hate Speech Roman Urdu 2020 corpus. The tweets in the this corpus are classified at three levels: Neutral-Hostile, Simple-Complex, and Offensive-Hate speech. As another contribution, we have used five supervised learning techniques, including a deep learning technique, to evaluate and compare their effectiveness for hate speech detection. The results show that Logistic Regression outperformed all other techniques, including deep learning techniques for the two levels of classification, by achieved an F1 score of 0.906 for distinguishing between Neutral-Hostile tweets, and 0.756 for distinguishing between Offensive-Hate speech tweets.

中文翻译:

罗马乌尔都语中的仇恨言论检测

仇恨言论是一种特定类型的有争议的内容,被广泛立法为必须识别和阻止的犯罪。但是,由于 Twitter 数据流的庞大数量和速度,仇恨言论检测无法手动执行。为了解决这个问题,已经对欧洲语言中的仇恨言论检测进行了几项研究,而对资源匮乏的南亚语言却很少关注,这使得社交媒体容易受到数百万用户的攻击。特别是,据我们所知,尚未对在次大陆广泛使用的罗马乌尔都语文本中的仇恨言论检测进行研究。在这项研究中,我们删除了 90,000 多条推文并手动解析它们以识别 5,000 条罗马乌尔都语推文。随后,我们采用迭代方法来制定指南,并使用它们来生成仇恨言论罗马乌尔都语 2020 语料库。该语料库中的推文分为三个级别:中立-敌对、简单-复杂和攻击性仇恨言论。作为另一个贡献,我们使用了五种监督学习技术,包括一种深度学习技术,来评估和比较它们在仇恨言论检测方面的有效性。结果表明,Logistic 回归优于所有其他技术,包括用于两个分类级别的深度学习技术,在区分中立-敌对推文方面的 F1 得分为 0.906,在区分攻击性和仇恨语音推文方面的 F1 得分为 0.756。该语料库中的推文分为三个级别:中立-敌对、简单-复杂和攻击性仇恨言论。作为另一个贡献,我们使用了五种监督学习技术,包括一种深度学习技术,来评估和比较它们在仇恨言论检测方面的有效性。结果表明,Logistic 回归优于所有其他技术,包括用于两个分类级别的深度学习技术,在区分中立-敌对推文方面的 F1 得分为 0.906,在区分攻击性和仇恨语音推文方面的 F1 得分为 0.756。该语料库中的推文分为三个级别:中立-敌对、简单-复杂和攻击性仇恨言论。作为另一个贡献,我们使用了五种监督学习技术,包括一种深度学习技术,来评估和比较它们在仇恨言论检测方面的有效性。结果表明,Logistic 回归优于所有其他技术,包括用于两个分类级别的深度学习技术,在区分中立-敌对推文方面的 F1 得分为 0.906,在区分攻击性和仇恨语音推文方面的 F1 得分为 0.756。
更新日期:2021-03-09
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