| Challenge: | Existing methods to detect online hate speech ignore conversational context . generative hate speech intervention is a novel approach to counter online hate . |
| Approach: | They propose a task where generative hate speech intervention generates responses to intervene during online conversations that contain hate speech. |
| Outcome: | The proposed method can detect and block hate speech and discourage it . it can also generate responses written by Mechanical Turk workers . |
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Generating Counter Narratives against Online Hate Speech: Data and Strategies (2020.acl-main)
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| Challenge: | Hate Speech (HS) is a pervasive issue that spreads quickly and widely . research has focused on avoiding undesired effects that come with content moderation . |
| Approach: | They propose to use large scale unsupervised language models to generate responses to hate effectively using large scale models. |
| Outcome: | The proposed methods lack quality data and produce generic/repetitive responses. |
HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection (2020.coling-main)
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| Challenge: | Existing methods to detect online hate speech depend heavily on labeled datasets for training, which results in poor detection performance of the hate speech class. |
| Approach: | They propose a deep generative reinforcement learning model which augments two commonly-used hate speech detection datasets with the HateGAN generated tweets. |
| Outcome: | The proposed model improves the detection performance of hate speech class regardless of the classifiers and datasets used in the detection task. |
Directions for NLP Practices Applied to Online Hate Speech Detection (2022.emnlp-main)
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| Challenge: | Existing approaches to address hate speech in online spaces have relied on conventions and practices from NLP. |
| Approach: | They argue that many conventions in NLP are poorly suited for the problem and encourage researchers to develop methods that are more appropriate for the task. |
| Outcome: | The proposed methods are poorly suited for the problem and should be adapted to address the propagation of online harms. |
Uncovering the Root of Hate Speech: A Dataset for Identifying Hate Instigating Speech (2023.findings-emnlp)
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| Challenge: | a lack of comprehensive datasets specifically annotated for hate instigating speech hinders research . lack of reliable models for hate triggering makes it difficult to apply off-the-shelf models to the problem. |
| Approach: | They propose to use a multilingual dataset to identify hate instigating speech . lack of comprehensive datasets specifically annotated for hate instigators hinders their work . |
| Outcome: | The proposed dataset identifies hate instigating speech across languages . lack of comprehensive datasets makes it difficult to train and evaluate models . |
Improving Hate Speech Detection with Deep Learning Ensembles (L18-1)
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| Challenge: | censorship is a potential risk when addressing these issues with automated text classification methods. |
| Approach: | They propose to use a neural network-based ensemble method to better classify hate speech using a publicly available embedding model and a popular sentiment dataset. |
| Outcome: | The proposed method improves by 5 points on a hate speech corpus from Twitter and a popular sentiment dataset. |
LLM generated responses to mitigate the impact of hate speech (2024.findings-emnlp)
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Jakub Podolak, Szymon Łukasik, Paweł Balawender, Jan Ossowski, Jan Piotrowski, Katarzyna Bakowicz, Piotr Sankowski
| Challenge: | a study aims to determine the effectiveness of large language models to counteract hate speech . it is the first real-life A/B test evaluating the effectiveness . |
| Approach: | They conduct the first real-life A/B test assessing the effectiveness of LLM-generated counter-speech. |
| Outcome: | The proposed system reduces user engagement by over 20%, the study shows . the proposed metric is based on a simple metric and is scalable to other platforms . |
Countering Hateful and Offensive Speech Online - Open Challenges (2024.emnlp-tutorials)
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Leon Derczynski, Marco Guerini, Debora Nozza, Flor Miriam Plaza-del-Arco, Jeffrey Sorensen, Marcos Zampieri
| Challenge: | a comprehensive understanding of the field is needed to maintain respectful and inclusive online environments. |
| Approach: | 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. |
| Outcome: | 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. |
Latent Hatred: A Benchmark for Understanding Implicit Hate Speech (2021.emnlp-main)
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Mai ElSherief, Caleb Ziems, David Muchlinski, Vaishnavi Anupindi, Jordyn Seybolt, Munmun De Choudhury, Diyi Yang
| Challenge: | Existing studies on explicit or overt hate speech have failed to address a more pervasive form based on coded or indirect language. |
| Approach: | They propose a theoretically-justified taxonomy of implicit hate speech and a benchmark corpus with fine-grained labels for each message and its implication. |
| Outcome: | The proposed dataset will serve as a useful benchmark for understanding this multifaceted issue. |
Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection (N18-2)
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| Challenge: | Existing methods that focus on a single tweet as input are likely to yield high false positive and negative rates. |
| Approach: | They propose a model that leverages intra-user and inter-user representation learning to improve hate speech detection on Twitter by suppressing the noise in a single Tweet. |
| Outcome: | The proposed model significantly improves the f-score of a strong bidirectional LSTM model by 10.1%. |
Integrating Argumentation and Hate-Speech-based Techniques for Countering Misinformation (2024.emnlp-main)
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| Challenge: | scalable strategies to combat online misinformation are short-term and insufficient, authors say . current reactive approaches, like content flagging and banning, do little to change perception of misinformants . human evaluations show that our framework generates expert-like responses . |
| Approach: | They propose a framework that generates persuasive responses from hate-speech counter-responses . human evaluations show that the framework generates expert-like responses . |
| Outcome: | The proposed framework generates expert-like responses and is 14% more engaging, 21% more natural, and 18% more factual than the best available alternatives. |