Automated hate speech detection is an important tool in combating the spread of hate
speech, particularly in social media. Numerous methods have been developed for the task …

Large language models (LLMs) are reported to be partial to certain cultures owing to the
training data dominance from the English corpora. Since multilingual cultural data are often …

Numerous datasets have been proposed to combat the spread of online hate. Despite these
efforts, a majority of these resources are English-centric, primarily focusing on overt forms of …

Korean is often referred to as a low-resource language in the research community. While
this claim is partially true, it is also because the availability of resources is inadequately …

With the growth of online services, the need for advanced text classification algorithms, such
as sentiment analysis and biased text detection, has become increasingly evident. The …

Cultural bias is pervasive in many large language models (LLMs), largely due to the
deficiency of data representative of different cultures. Typically, cultural datasets and …

Offensive language detection is an important task for filtering out abusive expressions and
improving online user experiences. However, malicious users often attempt to avoid filtering …

In response to the phenomenon of hate speech on popular social media such as Facebook,
a number of researchers have investigated and developed different automated techniques …

Developed from a graduate course this book examines some of the deep issues that have
become very relevant due to the increasing use of AI in recent times. The book discusses …

The proliferation of hate speech (HS) has compromised the safety and trustworthiness of the
internet, exacerbating social divides by promoting hatred and discrimination. Although …