| Challenge: | Existing methods to detect offensive language have been limited by categorical labels . however, there are several challenges in the detection of such content . |
| Approach: | They analyze Reddit comments with fine-grained, real-valued offensiveness scores . they evaluate the ability of widely-used neural models to predict offensiveness . |
| Outcome: | The proposed method produces highly reliable offensiveness scores and can predict scores on reddit comments. |
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The Relevance of Value Systems for Offensive Language Detection (2026.eacl-long)
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| Challenge: | Recent research in perspectivism has departed from the assumption that offensiveness can be defined through a universal perspective. |
| Approach: | They propose to use a dataset consisting of neutrally-phrased sentences on controversial topics, evaluated by individuals from 4 different value systems to identify offensiveness patterns. |
| Outcome: | The proposed dataset consists of neutrally-phrased sentences on controversial topics, evaluated by individuals from 4 different value systems. |
Multilingual Content Moderation: A Case Study on Reddit (2023.eacl-main)
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| Challenge: | a growing need for AI moderators to safeguard users and protect mental health of human moderator from traumatic content. |
| Approach: | They propose to use a multilingual dataset to study the challenges of content moderation . they propose to analyze 1.8 million Reddit comments in English, german, spanish and french . |
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Offensive Language and Hate Speech Detection for Danish (2020.lrec-1)
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| Challenge: | a growing number of social media platforms are detecting and dealing with offensive language . a recent study found that the best performing system for English is best for Danish . |
| Approach: | They propose automatic methods to detect offensive language on social media platforms . they use user-generated comments from various social media sites to find offensive language . |
| Outcome: | The proposed system performs best for both English and Danish language . it achieves a macro averaged F1-score of 0.74 and a best for Danish achieves 0.73 . |
OLEA: Tool and Infrastructure for Offensive Language Error Analysis in English (2023.eacl-demo)
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| Challenge: | State-of-the-art models for identifying offensive language fail to generalize over nuanced or implicit cases of offensive and hateful language. |
| Approach: | They propose an open-source Python library for error analysis in the context of offensive language detection. |
| Outcome: | OLEA provides tools for error analysis in the context of detecting offensive language in English. |
On the Robustness of Offensive Language Classifiers (2022.acl-long)
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| Challenge: | Existing studies on offensive language classifiers have focused on primitive attacks such as misspellings and extraneous spaces. |
| Approach: | They analyze the robustness of offensive language classifiers against crafty adversarial attacks that leverage greedy- and attention-based word selection and context-aware embeddings for word replacement. |
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RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models (2021.acl-long)
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| Challenge: | Recent work has focused on measuring and mitigating bias in pretrained language models. |
| Approach: | They propose a dataset that measures and mitigates bias across gender,race, religion, and queerness . they compare REDDITBIAS to a widely used conversational DialoGPT model . |
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Introducing CAD: the Contextual Abuse Dataset (2021.naacl-main)
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| Challenge: | Detecting and classifying online abuse is a complex and nuanced task, despite many advances in the power and availability of computational tools. |
| Approach: | They propose to annotate a reddit conversation thread with six distinct primary and secondary categories and an expert-driven group-adjudication process for high quality annotations. |
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Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to Analysis (2024.naacl-long)
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| Challenge: | Existing datasets for hate speech detection neglect the cultural diversity within a single language. |
| Approach: | They propose a CR**oss-cultural **E**nglish **Hate* speech dataset that uses culturally hateful keywords to identify posts from four countries plus the United States. |
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Toxic, Hateful, Offensive or Abusive? What Are We Really Classifying? An Empirical Analysis of Hate Speech Datasets (2020.lrec-1)
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| Challenge: | a recent study shows that many definitions are being used for equivalent concepts, making most datasets incompatible. |
| Approach: | They analyze six publicly available datasets to determine their similarity and compatibility . they propose to use Fast Text word vectors to analyze similarity between different datasets . |
| Outcome: | The proposed model performs better on similar datasets and worse on more non-offensive samples. |
“Why do I feel offended?” - Korean Dataset for Offensive Language Identification (2023.findings-eacl)
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| Challenge: | Existing methods for detecting offensive content rely on labeled datasets, but few consider low-resource languages with relatively less data available for training. |
| Approach: | They propose to use Korean as a dataset for offensive language identification . they propose to perform abusive language detection and sentiment analysis to help identify offensive languages. |
| Outcome: | The proposed datasets improve the performance of offensive language identification in Korean, while the existing methods are limited. |