Countering Hateful and Offensive Speech Online - Open Challenges (2024)

Abstract: In today’s digital age, hate speech and offensive speech online pose a significant challenge to maintaining respectful and inclusive online environments. This tutorial aims to provide attendees with a comprehensive understanding of the field by delving into essential dimensions such as multilingualism, counter-narrative generation, a hands-on session with one of the most popular APIs for detecting hate speech, fairness, and ethics in AI, and the use of recent advanced approaches. In addition, the tutorial aims to foster collaboration and inspire participants to create safer online spaces by detecting and mitigating hate speech.

## description hate speech (hs) refers to any form of communication that belittles or targets individuals or groups based on characteristics such as race, color, ethnicity, gender, sexual orientation, nationality, religion, or other defining features. 1 this problem has experienced a rapid surge on the web, especially on social media platforms, and contributes to the perpetuation of discrimination, division, and hostility in our society. consequently, the need to identify and combat this issue has become increasingly imperative. automatic countering of hs and offensive language in natural language processing (nlp) have experienced a surge in popularity since the 2010s, leading to the emergence of diverse resources and tasks within the community @xcite @xcite . these range from conventional machine learning techniques using classifiers such as support vector machines and logistic regression, to classification models based on the transformer architecture, such as bert or roberta @xcite . more recently, 1 https://ec.europa.eu/commission/ presscorner/detail/en/ip_22_7109 large language models (llms) have emerged as a promising alternative to address the challenges of supervised learning, using strategies like zero-shot and few-shot learning via prompting @xcite . hs countering faces considerable obstacles, particularly when dealing with languages or contexts that lack sufficient labeled data @xcite . additionally, hs is a subjective and context-dependent phenomenon, shaped by factors like demographics, social norms, cultural backgrounds @xcite . as a result, addressing this subjectivity has become a growing focus of research, with increasing attention given to incorporating multilingualism in the development of models and resources for detecting hate speech @xcite and considering different vantage points @xcite . while recent advancements in language models have demonstrated remarkable abilities in detecting such content, there is also a concerning observation that these models tend to capture and perpetuate biases, for instance, harmful stereotypes @xcite @xcite @xcite . this tutorial aims to provide participants with a comprehensive understanding of countering hate speech and offensive language in nlp by delving into essential dimensions, including multilingualism, counter-narrative generation, practical sessions with the popular perspective api, fairness and ethics, and the role of recent advances approaches with llms. ## type of tutorial this tutorial aims to present introductory nlp research on hate speech detection. specifically, it will cover fundamental concepts related to hate speech, dataset creation, methodologies, techniques, practical sessions, and ethical considerations. ## pre-requisites this tutorial caters to a diverse audience: nlp researchers who are currently involved in nlp for social good or have a strong interest in how to address hate speech detection in textual data; industry practitioners working in social media, online platforms, content moderation, and related domains that would like to have a general vision about how to combat hate speech; students, academics, and organizations interested in gaining insights about nlp techniques for hate speech detection. foot_0 ## outline the tutorial will be 3.5 hours, including a half-hour coffee break. over the course of this tutorial, we will delve into five key components. ## reading list we recommend that attendees read the following works: jeffrey sorensen jeffrey was part of the original team at jigsaw that launched the perspective api. jeff joined google in 2010 to work with the speech team, developing compact language models for use in on-device recognizer for mobile devices, and lead a team responsible for data collection and annotation. jeffrey sorensen has worked on machine learning models for speech recognition and translation, both for google and previously for ibm. ## diversity considerations our tutorial strongly values diversity as we focus on combating online abuse, hate, and related issues. our diversity efforts include: 1) inviting participation from various fields beyond nlp; 2) reaching out to underrepresented nlp scholars; and 3) forming a diverse organizing committee that embodies a wide range of backgrounds, experiences, and viewpoints, enriching the tutorial's guidance and impact. ## audience size estimation considering the historical attendance record of the related workshop on online abuse and harms (woah), coupled with the increasing societal and research focus on addressing online abuse, we anticipate a participation of 60-80 attendees. ## tutorial materials the tutorial materials are publicy available on github. 3 ## ethics statement our goal is to provide attendees with tools and knowledge to address these issues responsibly and enhance online safety. throughout the tutorial, we will emphasize the importance of ethical considerations in hate speech detection and mitigation. we will explore not only the technical aspects but also the broader social and ethical implications of deploying hate speech detection systems. in addition, we are committed to promoting fairness, transparency, and accountability in the development and use of hate speech countering technologies. we will discuss the challenges posed by harmful biases and stereotypes in training data and the importance of identifying and mitigating these issues across the nlp models. responsible and ethical approaches are essential to creating a positive impact in the field of hate speech countering. ameya vaidya, feng mai, and yue ning. 2020. empirical analysis of multi-task learning for reducing identity bias in toxic comment detection. in proceedings of the international aaai conference on web and social media, volume 14, pages 683-693. bertie vidgen and leon derczynski. 2021. directions in abusive language training data, a systematic review: garbage in, garbage out. plos one, 15(12):1-32. zeerak waseem and dirk hovy. 2016. hateful symbols or hateful people? predictive features for hate speech detection on twitter. in proceedings of the naacl student research workshop, pages 88-93, san diego, california. association for computational linguistics. tharindu weerasooriya, sujan dutta, tharindu ranasinghe, marcos zampieri, christopher homan, and ashiqur khudabukhsh. 2023. vicarious offense and noise audit of offensive speech classifiers: unifying human and machine disagreement on what is offensive. in proceedings of the 2023 conference on empirical methods in natural language processing (emnlp), pages 11648-11668. marcos zampieri, shervin malmasi, preslav nakov, sara rosenthal, noura farra, and ritesh kumar. 2019. predicting the type and target of offensive posts in social media. in proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pages 1415-1420, minneapolis, minnesota. association for computational linguistics. marcos zampieri, preslav nakov, sara rosenthal, pepa atanasova, georgi karadzhov, hamdy mubarak, leon derczynski, zeses pitenis, and çagrı çöltekin. 2020. semeval-2020 task 12: multilingual offensive language identification in social media (offenseval 2020). in proceedings of the fourteenth workshop on semantic evaluation, pages 1425-1447, barcelona (online). international committee for computational linguistics.