Since the proliferation of social media usage, hate speech has become a major crisis. On the one hand, hateful content creates an unsafe environment for certain members of our society. On the other hand, in-person moderation of hate speech causes distress to content moderators. Additionally, it is not just the presence of hate speech in isolation but its ability to dissipate quickly, where early detection and intervention can be most effective. Through this course, we will provide a holistic view of hate speech detection mechanisms explored so far.

In this course, I will start by talking about why studying hate speech detection is very important. I will then talk about a collection of many hate speech datasets. We will discuss the different forms of hate labels that such datasets incorporate, their sizes and sources. Next, we will talk about feature based and traditional machine learning methods for hate speech detection. More recently since 2017, deep learning methods have been proposed for hate speech detection. Hence, we will talk about traditional deep learning methods. Next, we will talk about deep learning methods focusing on specific aspects of hate speech detection like multi-label aspect, training data bias, using metadata, data augmentation, and handling adversarial attacks. After this, we will talk about multimodal hate speech detection mechanisms to handle image, text and network based inputs. We will discuss various ways of mode fusion. Next, we will talk about possible ways of building interpretations over predictions from a deep learning based hate speech detection model. Finally, we will talk about challenges and limitations of current hate speech detection models. We will conclude the course with a brief summary.