“HOT” ChatGPT: The Promise of ChatGPT in Detecting and Discriminating Hateful, Offensive, and Toxic Comments on Social Media

Li, Lingyao; Fan, Lizhou; Atreja, Shubham; Hemphill, Libby

doi:10.1145/3643829

Cited by 26 publications

(2 citation statements)

References 75 publications

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“…Researchers are examining the potential of GPT-3 models for the classification of hateful content (Chiu, Collins and Alexander 2022;Wang and Chang 2022;Huang, Kwak and An 2023). Li et al (2023) conduct extensive prompting experiments and compare the performance of ChatGPT to that of crowdworkers for the task of classifying texts as hateful, offensive or toxic (HOT). They find that ChatGPT achieves an accuracy of roughly 80% when compared to crowdworkers' annotations.…”

Section: Text Classification With Prompt-based Generative Modelsmentioning

confidence: 99%

8. Algorithms Against Antisemitism?

Steffen,

Pustet,

Mihaljević

2024

Antisemitism in Online Communication

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The proliferation of hateful and violent speech in online media underscores the need for technological support to combat such discourse, create safer and more inclusive online environments, support content moderation and study political-discourse dynamics online. Automated detection of antisemitic content has been little explored compared to other forms of hate-speech. This chapter examines the automated detection of antisemitic speech in online and social media using a corpus of online comments sourced from various online and social media platforms. The corpus spans a three-year period and encompasses diverse discourse events that were deemed likely to provoke antisemitic reactions. We adopt two approaches. First, we explore the efficacy of Perspective API, a popular content- moderation tool that rates texts in terms of, e.g., toxicity or identity-related attacks, in scoring antisemitic content as toxic. We find that the tool rates a high proportion of antisemitic texts with very low toxicity scores, indicating a potential blind spot for such content. Additionally, Perspective API demonstrates a keyword bias towards words related to Jewish identities, which could result in texts being falsely flagged and removed from platforms. Second, we fine-tune deep learning models to detect antisemitic texts. We show that OpenAI’s GPT-3.5 can be fine-tuned to effectively detect antisemitic speech in our corpus and beyond, with F1 scores above 0.7. We discuss current achievements in this area and point out directions for future work, such as the utilisation of prompt-based models.

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Section: Text Classification With Prompt-based Generative Modelsmentioning

confidence: 99%

8. Algorithms Against Antisemitism?

Steffen,

Pustet,

Mihaljević

2024

Antisemitism in Online Communication

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show abstract

Section: Text Classification With Prompt-based Generative Modelsmentioning

confidence: 99%

Antisemitism in Online Communication

2024

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The normalisation of hate speech, including antisemitic rhetoric, poses a significant threat to social cohesion and democracy. While global efforts have been made to counter contemporary antisemitism, there is an urgent need to understand its online manifestations. Hate speech spreads easily across the internet, facilitated by anonymity and reinforced by algorithms that favour engaging--even if offensive--content. It often takes coded forms, making detection challenging. Antisemitism in Online Communication addresses these issues by analysing explicit and implicit antisemitic statements in mainstream online discourse. Drawing from disciplines such as corpus linguistics, computational linguistics, semiotics, history, and philosophy, this edited collection examines over 100,000 user comments from three language communities. Contributors explore various facets of online antisemitism, including its intersectionality with misogyny and its dissemination through memes and social networks. Through case studies, they examine the reproduction, support, and rejection of antisemitic tropes, alongside quantitative assessments of comment structures in online discussions. Additionally, the volume delves into the capabilities of content moderation tools and deep-learning models for automated hate speech detection. This multidisciplinary approach provides a comprehensive understanding of contemporary antisemitism in digital spaces, recognising the importance of addressing its insidious spread from multiple angles.

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Hate speech detection in social media: Techniques, recent trends, and future challenges

Rawat,

Kumar,

Samant

2024

WIREs Computational Stats

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The realm of Natural Language Processing and Text Mining has seen a surge in interest from researchers in hate speech detection, leading to an increase in related studies. This analysis aims to create a valuable resource by summarizing the methods and strategies used to combat hate speech in social media. We perform a detailed review to achieve a deep knowledge of the hate speech detection landscape from 2018 to 2023, revealing global incidents of hate speech in 2022–2023. Sixty‐six relevant articles were selected for this review. Existing studies were analyzed and categorized into five method categories: Machine Learning, Deep Learning, Ensemble models, Graph Neural Networks, and Graph Convolutional Networks. These advancements can aid social networking services in identifying hate messages before being posted, reducing the risk of harassment. The review also covers available hate speech datasets and highlights research challenges, but it is clear that a definitive solution to this problem is yet to be found. Future research directions are recommended to address the ongoing challenges in Hate Speech Detection.This article is categorized under: Applications of Computational Statistics > Computational Linguistics Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery Statistical Learning and Exploratory Methods of the Data Sciences > Classification and Regression Trees (CART) Statistical Learning and Exploratory Methods of the Data Sciences > Text Mining

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“HOT” ChatGPT: The Promise of ChatGPT in Detecting and Discriminating Hateful, Offensive, and Toxic Comments on Social Media

Cited by 26 publications

References 75 publications

8. Algorithms Against Antisemitism?

8. Algorithms Against Antisemitism?

Antisemitism in Online Communication

Hate speech detection in social media: Techniques, recent trends, and future challenges

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