mlsanityData to 14 Oct 2026

Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection

arXiv 2412.11745 ↗First posted 16 Dec 2024cs.CLcited by 3

Not on the map. Why?

Abstract (arXiv)

The proliferation of radical content on online platforms poses significant risks, including inciting violence and spreading extremist ideologies. Despite ongoing research, existing datasets and models often fail to address the complexities of multilingual and diverse data. To bridge this gap, we introduce a publicly available multilingual dataset annotated with radicalization levels, calls for action, and named entities in English, French, and Arabic. This dataset is pseudonymized to protect individual privacy while preserving contextual information. Beyond presenting our freely available dataset, we analyze the annotation process, highlighting biases and disagreements among annotators and their implications for model performance. Additionally, we use synthetic data to investigate the influence of socio-demographic traits on annotation patterns and model predictions. Our work offers a comprehensive examination of the challenges and opportunities in building robust datasets for radical content detection, emphasizing the importance of fairness and transparency in model development.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 35 of its 90 references that are in the map. First posted on arXiv 16 Dec 2024.

Builds on

35 of 90 references.

References · 35

  1. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  2. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,125
  3. 2023Whose Opinions Do Language Models Reflect?cited by 773
  4. 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 752
  5. 2018A Reductions Approach to Fair Classificationcited by 587
  6. 2022Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studiescited by 515
  7. 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 437
  8. 2021Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotationscited by 314
  9. 2021Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detectioncited by 264
  10. 2023PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traitscited by 234
  11. 2024Quantifying the Persona Effect in LLM Simulationscited by 169
  12. 2021XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyondcited by 114
23 more
  1. 2023When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Taskscited by 90
  2. 2020Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLPcited by 50
  3. 2023Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to Analysiscited by 45
  4. 2024VariErr NLI: Separating Annotation Error from Human Label Variationcited by 42
  5. 2024The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labelscited by 38
  6. 2023Can Large Language Models Capture Dissenting Human Voices?cited by 33
  7. 2021Identifying Morality Frames in Political Tweets using Relational Learningcited by 26
  8. 2020Detecting White Supremacist Hate Speech using Domain Specific Word Embedding with Deep Learning and BERTcited by 19
  9. 2023Large Language Models as Subpopulation Representative Models: A Reviewcited by 16
  10. 2021Interpretable Multi-Modal Hate Speech Detectioncited by 10
  11. 2022Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection Modelscited by 10
  12. 2023Wisdom of Instruction-Tuned Language Model Crowds. Exploring Model Label Variationcited by 6
  13. 2024Different Tastes of Entities: Investigating Human Label Variation in Named Entity Annotationscited by 5
  14. 2018Automatic Detection of Online Jihadist Hate Speechcited by 4
  15. 2019Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twittercited by 4
  16. 2024Assessing Generalization for Subpopulation Representative Modeling via In-Context Learningcited by 4
  17. 2020Establishing a New State-of-the-Art for French Named Entity Recognitioncited by 3
  18. 2021A survey on extremism analysis using Natural Language Processingcited by 3
  19. 2022Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinioncited by 3
  20. 2024Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Taskscited by 3
  21. 2024Can Humans Identify Domains?cited by 2
  22. 2024Jointly modelling the evolution of social structure and language in online communitiescited by 2
  23. 2019Detecting Radical Text over Online Media using Deep Learningcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025IYKYK: Using language models to decode extremist cryptolectscited by 3
  2. 2026Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via Consensus-Driven Preference Optimisationcited by 1
  3. 2026Semantically Orthogonal Framework for Citation Classification: Disentangling Intent and Contentcited by 0