From Emotion to Expression: Theoretical Foundations and Resources for Fear Speech (2026.eacl-long)
Copied to clipboard
| Challenge: | a new study of fear speech is under-resourced and fragmented. authors review existing definitions and propose a taxonomy that consolidates different dimensions of fear. |
| Approach: | They propose a taxonomy that consolidates different dimensions of fear for studying fear speech. |
| Outcome: | The proposed taxonomy consolidates different dimensions of fear for studying fear speech. |
Similar Papers
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. |
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 . |
Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets (2020.emnlp-main)
Copied to clipboard
| Challenge: | a recent study has shown that data collection is neglected by ignoring the quality of data. |
| Approach: | They propose to use latent semantics to evaluate selection bias in hate speech . they compare latent Dirichlet Allocation (LDA) to eleven hate speech corpora . |
| Outcome: | The proposed method could be revisable before focusing on classification performance. |
Darkness can not drive out darkness: Investigating Bias in Hate SpeechDetection Models (2022.acl-srw)
Copied to clipboard
| Challenge: | a recent study shows that machine learning models are biased and they might make the wrong decisions for the wrong reasons. |
| Approach: | They investigate the impact of social bias on the performance of hate speech detection models . they also investigate the causal effect of intersectional bias on models' unfairness . |
| Outcome: | The proposed model is biased and makes the wrong decisions for the wrong reasons. |
The Challenges of Creating a Parallel Multilingual Hate Speech Corpus: An Exploration (2024.lrec-main)
Copied to clipboard
| Challenge: | Hate speech is one of the most demanding topics in Natural Language Processing, as its multifaceted nature is accompanied by a handful of challenges, such as multilinguality and cross-linguality. |
| Approach: | They propose a pipeline that could be used to create a parallel multilingual hate speech dataset using machine translation. |
| Outcome: | The proposed pipeline will be able to create a parallel multilingual hate speech dataset using machine translation. |
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. |
Multilingual and Multi-Aspect Hate Speech Analysis (D19-1)
Copied to clipboard
| Challenge: | Current research on hate speech analysis is oriented towards monolingual and single classification tasks. |
| Approach: | They propose to use a multilingual multi-aspect hate speech analysis dataset to test current methods . they evaluate the dataset in various classification settings and discuss how to leverage annotations . |
| Outcome: | The proposed dataset can be used to improve hate speech detection and classification in general. |
When Words Wear Masks: Detecting Malicious Intents and Hostile Impacts of Online Hate Speech (2026.eacl-short)
Copied to clipboard
| Challenge: | Existing methods for hate speech detection treat hate speech as a monolithic phenomenon, ignoring the speaker’s motivations and potential societal consequences. |
| Approach: | They propose a dataset with a dual taxonomy that separates Intent (why the speaker produced hate speech) and Impact (what harm it may cause to individuals and communities) they propose to use this data to enable content moderation and user safety. |
| Outcome: | The proposed dataset captures Intent (why the speaker produced hate speech) and Impact (what harm it may cause to individuals and communities) of online hateful posts. |
Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions (2024.lrec-main)
Copied to clipboard
| Challenge: | Emotion analysis (EA) is a rapidly growing field in natural language processing . there is no consensus on scope, direction, or methods for EA . |
| Approach: | They review 154 relevant NLP papers on emotion analysis from the last decade . they ask: how are EA tasks defined in NLP? what are the most prominent emotion frameworks and which emotions are modeled? |
| Outcome: | The authors examine 154 relevant NLP papers on emotion analysis from the last decade . they find that there is no consensus on scope, direction, or methods . |
Uncovering the Root of Hate Speech: A Dataset for Identifying Hate Instigating Speech (2023.findings-emnlp)
Copied to clipboard
| 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 . |