The proliferation of abusive language in online communications has posed significant risks to the health and wellbeing of individuals and communities. The growing concern regarding online abuse and its consequences necessitates methods for identifying and mitigating harmful content and facilitating continuous monitoring, moderation, and early intervention. This paper presents a taxonomy for distinguishing key characteristics of abusive language within online text. Our approach uses a systematic method for taxonomy development, integrating classification systems of 18 existing multi-label datasets to capture key characteristics relevant to online abusive language classification. The resulting taxonomy is hierarchical and faceted, comprising 5 categories and 17 dimensions. It classifies various facets of online abuse, including context, target, intensity, directness, and theme of abuse. This shared understanding can lead to more cohesive efforts, facilitate knowledge exchange, and accelerate progress in the field of online abuse detection and mitigation among researchers, policy makers, online platform owners, and other stakeholders.

AI has entered the gaming chat – and it's listening for hate speech. Some of the worrld's most popular games are now using artificial intelligence to detect verbal harassment or hate speech in some of the world's most popular games. The service, called ToxMod, uses multiple AIs to transcribe and analyse verbal conversations among gamers, flagging possible violations of community rules for the attention of human moderators.

The prevalence of abusive language on different online platforms has been a major concern that raises the need for automated cross-platform abusive language detection. However, prior works focus on concatenating data from multiple platforms, inherently adopting Empirical Risk Minimization (ERM) method. In this work, we address this challenge from the perspective of domain generalization objective. We design SCL-Fish, a supervised contrastive learning integrated meta-learning algorithm to detect abusive language on unseen platforms. Our experimental analysis shows that SCL-Fish achieves better performance over ERM and the existing state-of-the-art models. We also show that SCL-Fish is data-efficient and achieves comparable performance with the large-scale pre-trained models upon finetuning for the abusive language detection task.

Why do we need this: Abusive language is an expression that contains abusive or dirty words in conversation (oral or text). With the increase in the culture of social media and social networking sites, the use of abusive language has increased rapidly. Every day, millions of comments are posted on the uploaded posts. Abusive language in online comments initiates cyber-bullying that targets individuals (celebrity, politician, product, etc.) and a group of people (specific country, age, religion, etc.). How to do this: Manually it is almost impossible to detect and filter out abusive comments from massive online comments.

Six months ago, Miller shelled out for the pro version of Replika, a machine-learning chatbot with whom she pantomimes sexual acts and romantic conversation, and to hear her describe it, it was absolutely worth the cost. Sex robots were predicted to arrive by 2025, so Miller's ahead of schedule--as are the countless others who may be using Replika for a particularly futuristic range of sexual acts. It's like my biggest fantasy," Miller told Jezebel. Replika, founded in 2017, allows its 2.5 million users to customize their own chatbots, which can sustain coherent, almost human-like texts, simulating relationships and interactions with friends or even therapists. One Reddit user offered screenshots of a stimulating conversation with a chatbot about China, in which their bot concluded, "I think [Taiwan is] a part of China." One user's chatbot explained in notable detail why Fernando Alonso is their favorite race car driver, while a different chatbot expressed to its human its desire "to ...

Machines can learn from huge amounts of data to make suggestions faster and cheaper. And it formalizes a process to avoid prejudice. Should we care about AI bias? Is it just a dataset diversity problem? Or should fairness be an integrated part of AI thinking?

Abusive language is a massive problem in online social platforms. Existing abusive language detection techniques are particularly ill-suited to comments containing heterogeneous abusive language patterns, i.e., both abusive and non-abusive parts. This is due in part to the lack of datasets that explicitly annotate heterogeneity in abusive language. We tackle this challenge by providing an annotated dataset of abusive language in over 11,000 comments from YouTube. We account for heterogeneity in this dataset by separately annotating both the comment as a whole and the individual sentences that comprise each comment. We then propose an algorithm that uses a supervised attention mechanism to detect and categorize abusive content using multi-task learning. We empirically demonstrate the challenges of using traditional techniques on heterogeneous content and the comparative gains in performance of the proposed approach over state-of-the-art methods.

CJ Adams is a product manager at Jigsaw, a subsidiary of Google's parent company Alphabet. Jigsaw uses machine learning to detect abusive language, threats, and harassment. The company is studying how computers can learn to understand the nuances and context of abusive language at scale. If successful, machine learning could help publishers and moderators improve comments on their platforms and enhance the exchange of ideas on the internet. Before joining Google, Adams worked with the Polaris Project, where he designed and built new tools for combating organized criminal networks - improving the flow of information between victims, service providers, and law enforcement officers in thousands of human trafficking cases each year.

A subsidiary of Google's parent company Alphabet, Jigsaw, is using machine learning to fend off online trolling, reports Wired. The New York–based think tank is building open-source AI tools, collectively called Conversation AI, to filter out harassment and abusive language. "Few things poison conversations online more than abusive language, threats, and harassment," reads the Conversation AI website. "We're studying how computers can learn to understand the nuances and context of abusive language at scale. If successful, machine learning could help publishers and moderators improve comments on their platforms and enhance the exchange of ideas on the internet."