Annotating for Hate Speech: The MaNeCo Corpus and Some Input from Critical Discourse Analysis (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for detecting hate speech are based on the problem of identification, but there is no clear definition of hate speech. |
| Approach: | They propose a multi-layer annotation scheme for the detection of hate speech in a web 2.0 corpus . they propose to use a binary hate speech classification to identify hate speech . |
| Outcome: | The proposed scheme is piloted against a binary hate speech classification and appears to yield higher inter-annotator agreement. |
Similar Papers
An Italian Twitter Corpus of Hate Speech against Immigrants (L18-1)
Copied to clipboard
| Challenge: | a recent study has annotated 6,000 tweets for hate speech against immigrants . the annotation scheme was designed to account for the multiplicity of factors that can contribute to the definition of a hate speech notion . |
| Approach: | They describe a Twitter corpus annotated for hate speech against immigrants . they propose a scheme that includes aggressiveness, offensiveness, irony, stereotype and intensity . |
| Outcome: | The proposed annotation scheme includes aggressiveness, offensiveness, irony, stereotype, intensity and (on an experimental basis) intensity. |
Untangling Hate Speech Definitions: A Semantic Componential Analysis Across Cultures and Domains (2025.findings-naacl)
Copied to clipboard
| Challenge: | a new framework for analyzing hate speech definitions is proposed to address cultural differences in interpretations . a dataset of 493 definitions from more than 100 cultures is used to analyze hate speech . |
| Approach: | They propose a framework for a cross-cultural and cross-domain analysis of hate speech definitions . they use open-source LLMs to analyze the impact of different definitions on hate speech detection . |
| Outcome: | The proposed framework enables cross-cultural and cross-domain analysis of hate speech definitions . it reveals that many domains borrow definitions from one another without taking into account target culture . |
HateBRXplain: A Benchmark Dataset with Human-Annotated Rationales for Explainable Hate Speech Detection in Brazilian Portuguese (2025.coling-main)
Copied to clipboard
| Challenge: | Hate speech detection systems have been developed to inhibit offensive and hateful language from being published or spread on the Web and social media. |
| Approach: | They propose to use a Portuguese dataset to provide rationales for hate speech detection with text span annotations. |
| Outcome: | The proposed models outperform the baselines in Portuguese and showed that they provide plausible explanations when compared to human annotations. |
HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection (2022.lrec-1)
Copied to clipboard
| Challenge: | In Brazil, hate speech is prohibited, however the regulation is not effective due to the difficulty of identifying, quantifying and classifying this kind of online content. |
| Approach: | They propose to annotate a large corpus of Brazilian Instagram comments manually and to use it to detect hate speech and offensive language. |
| Outcome: | The HateBR corpus was collected from the comment section of Brazilian politicians’ accounts on Instagram and manually annotated by specialists, reaching a high inter-annotator agreement. |
Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments (2022.findings-acl)
Copied to clipboard
| Challenge: | XTREMESPEECH dataset contains 20,297 social media passages from Brazil, Germany, India and Kenya . |
| Approach: | They propose a hate speech dataset containing 20,297 social media passages from Brazil, Germany, India and Kenya. |
| Outcome: | The proposed dataset contains 20,297 social media passages from Brazil, Germany, India and Kenya. |
Directions for NLP Practices Applied to Online Hate Speech Detection (2022.emnlp-main)
Copied to clipboard
| 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. |
Explain the Flag: Contextualizing Hate Speech Beyond Censorship (2026.findings-acl)
Copied to clipboard
Jason Liartis, Eirini Kaldeli, Lamprini Gyftokosta, Eleftherios Chelioudakis, Orfeas Menis Mastromichalakis
| Challenge: | a hybrid approach to detect and explain hate speech combines large language models with vocabularies to detect hate speech in three languages . authors: the spread of hate speech online has serious personal, social, and legal consequences . eu has launched initiatives to analyze, regulate, and counteract online hate speech, authors say . |
| Approach: | They propose a hybrid approach that combines Large Language Models with vocabularies to detect hate speech in English, French, and Greek. |
| Outcome: | The proposed approach outperforms baselines in English, French, and Greek . it uses large language models and vocabularies to detect and explain hate speech . human evaluation shows that the proposed approach is accurate and clear . |
Latent Hatred: A Benchmark for Understanding Implicit Hate Speech (2021.emnlp-main)
Copied to clipboard
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. |
AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages (2025.naacl-long)
Copied to clipboard
Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Abinew Ali Ayele, David Ifeoluwa Adelani, Ibrahim Said Ahmad, Saminu Mohammad Aliyu, Paul Röttger, Abigail Oppong, Andiswa Bukula, Chiamaka Ijeoma Chukwuneke, Ebrahim Chekol Jibril, Elyas Abdi Ismail, Esubalew Alemneh, Hagos Tesfahun Gebremichael, Lukman Jibril Aliyu, Meriem Beloucif, Oumaima Hourrane, Rooweither Mabuya, Salomey Osei, Samuel Rutunda, Tadesse Destaw Belay, Tadesse Kebede Guge, Tesfa Tegegne Asfaw, Lilian Diana Awuor Wanzare, Nelson Odhiambo Onyango, Seid Muhie Yimam, Nedjma Ousidhoum
| Challenge: | Hate speech and abusive language are global phenomena that need sociocultural background knowledge to be understood, identified, and moderated. |
| Approach: | They propose to use a multilingual dataset to collect hate speech and abusive language in 15 African languages to help improve model performance. |
| Outcome: | The proposed datasets are based on tweets annotated by native speakers familiar with the regional culture and show that they perform well in low-resource settings. |
So Hateful! Building a Multi-Label Hate Speech Annotated Arabic Dataset (2024.lrec-main)
Copied to clipboard
| Challenge: | Social media enables widespread propagation of hate speech targeting groups based on ethnicity, religion, or other characteristics. |
| Approach: | They analyze 70,000 Arabic tweets to identify hate speech patterns and train models . 15% of tweets contain offensive language while 6% have hate speech . authors hope to prevent spread of hateful content on social media platforms . |
| Outcome: | The analysis of 70,000 Arabic tweets shows that 15% of tweets contain offensive language while 6% have hate speech . 10% of tweet provide verifiable factual claims, and 7% are deemed important . |