Hate speech, offensive language, sexism, racism, and other types of abusive behavior have become a common phenomenon in many online social media platforms. In recent years, such diverse abusive behaviors have been manifesting with increased frequency and levels of intensity. Despite social media's efforts to combat online abusive behaviors this problem is still apparent. In fact, up to now, they have entered an arms race with the perpetrators, who constantly change tactics to evade the detection algorithms deployed by these platforms. Such algorithms, not disclosed to the public for obvious reasons, are typically custom-designed and tuned to detect only one specific type of abusive behavior, but usually miss other related behaviors. In the present paper, we study this complex problem by following a more holistic approach, which considers the various aspects of abusive behavior. We focus on Twitter, due to its popularity, and analyze user and textual properties from different angles of abusive posting behavior. We propose a deep learning architecture, which utilizes a wide variety of available metadata, and combines it with automatically-extracted hidden patterns within the text of the tweets, to detect multiple abusive behavioral norms which are highly inter-related. The proposed unified architecture is applied in a seamless and transparent fashion without the need for any change of the architecture but only training a model for each task (i.e., different types of abusive behavior). We test the proposed approach with multiple datasets addressing different abusive behaviors on Twitter. Our results demonstrate high performance across all datasets, with the AUC value to range from 92 to 98 .
In the case of @cite_20 , they focus on the problem of hate speech detection, and specifically attempt to detect racism and sexism, by applying various deep learning architectures. These architectures include Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and FastText @cite_16 , combined with numerous features like TF-IDF and Bag of Words (BoW) vectors. Their LSTM classifier with random embeddings seems to get significantly improved performance compared to baseline methods.
@cite_16 @cite_20
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This paper explores a simple and efficient baseline for text classification. Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation. We can train fastText on more than one billion words in less than ten minutes using a standard multicore CPU, and classify half a million sentences among 312K classes in less than a minute. Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char word n-gram methods by 18 F1 points.