论文标题
Del HaTe：一个深度学习的可调节合奏，用于仇恨言论检测
DeL-haTE: A Deep Learning Tunable Ensemble for Hate Speech Detection
论文作者
论文摘要
在社交媒体上的在线仇恨言论最近已成为一个快速增长的问题。邪恶的团体已经在几个主流（Twitter和Facebook）和边缘（GAB，4chan，8chan等）的媒体上开发了大型内容交付网络，以传达针对个人和社区的仇恨信息。因此，解决这些问题已成为大规模社交媒体的重中之重。自动检测和仇恨内容分类的三个主要挑战是缺乏明确标记的数据，不断发展的词汇和词汇 - 主题标签，表情符号等 - 以及缺乏用于GAB等边缘插座的基线模型。在这项工作中，我们提出了一个具有三个主要贡献的新颖框架。 （a）我们设计了一个结合最先进方法的优势的深度学习模型的合奏，（b）我们将调音因子纳入该框架中，以利用转移学习来进行转移学习进行自动仇恨语音分类，例如GAB，例如GAB，例如（C）我们开发了一种较弱的学习方法，可以训练无网e型数据培训框架。我们的合奏模型在HON数据集上实现了83％的仇恨召回，超过了最先进的深层模型的性能。我们证明，与分类器调整相结合的较弱的监督培训可显着提高GAB未标记数据的模型性能，从而达到了67％的仇恨召回。
Online hate speech on social media has become a fast-growing problem in recent times. Nefarious groups have developed large content delivery networks across several main-stream (Twitter and Facebook) and fringe (Gab, 4chan, 8chan, etc.) outlets to deliver cascades of hate messages directed both at individuals and communities. Thus addressing these issues has become a top priority for large-scale social media outlets. Three key challenges in automated detection and classification of hateful content are the lack of clearly labeled data, evolving vocabulary and lexicon - hashtags, emojis, etc. - and the lack of baseline models for fringe outlets such as Gab. In this work, we propose a novel framework with three major contributions. (a) We engineer an ensemble of deep learning models that combines the strengths of state-of-the-art approaches, (b) we incorporate a tuning factor into this framework that leverages transfer learning to conduct automated hate speech classification on unlabeled datasets, like Gab, and (c) we develop a weak supervised learning methodology that allows our framework to train on unlabeled data. Our ensemble models achieve an 83% hate recall on the HON dataset, surpassing the performance of the state-of-the-art deep models. We demonstrate that weak supervised training in combination with classifier tuning significantly increases model performance on unlabeled data from Gab, achieving a hate recall of 67%.
