#hate-speech-detection News & Analysis

6 articles tagged with #hate-speech-detection. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AINeutralarXiv – CS AI · Jun 236/10

🧠

ToxSyn-PT: A Synthetic Fine-Grained Dataset of Minority-Targeted Toxic Language in Portuguese

Researchers introduce ToxSyn-PT, a large-scale Portuguese dataset for detecting hate speech targeting minority groups, featuring fine-grained annotations and non-toxic counterexamples absent in existing datasets. The study reveals that hate speech detection models trained on social media fail to generalize to minority-specific contexts, exposing critical gaps in current evaluation metrics and highlighting the need for specialized datasets in non-English languages.

🏢 Hugging Face

AINeutralarXiv – CS AI · Jun 95/10

🧠

TeamHerald@CHIPSAL 2026: Hate Speech Detection and Sentiment Analysis of Nepali Memes using Transformer-based Architectures and Ensemble Learning

Researchers presented a study on detecting hate speech and analyzing sentiment in Nepali-language memes using transformer-based machine learning models and ensemble learning techniques. The work addresses challenges specific to Nepali text analysis, including code-mixing and limited baseline datasets, demonstrating that soft voting ensemble strategies outperform standalone models for multi-class sentiment tasks by 15.8% in Macro F1-score.

AINeutralarXiv – CS AI · Jun 96/10

🧠

Dealing with Annotator Disagreement in Hate Speech Classification

Researchers address the overlooked problem of annotator disagreement in hate speech classification, demonstrating that traditional approaches discarding non-consensus samples produce inflated performance metrics. The study establishes new state-of-the-art results for Turkish tweet classification by properly modeling disagreement as a valuable signal rather than noise, using aggregation methods and perceived hate speech strength scores to build more robust detection systems.

AINeutralarXiv – CS AI · Jun 16/10

🧠

Reasoning-Aware Multimodal Fusion for Hateful Video Detection

Researchers introduce RAMF (Reasoning-Aware Multimodal Fusion), a machine learning framework designed to detect hateful content in videos by combining visual, audio, and textual data with adversarial reasoning. The method achieves 3-7% performance improvements over existing approaches, addressing the challenge of identifying nuanced hate speech in increasingly complex online video content.

AINeutralarXiv – CS AI · May 296/10

🧠

TANDEM: Temporal-Aware Neural Detection for Multimodal Hate Speech

TANDEM introduces a unified framework for detecting hate speech in multimodal content by combining audio, visual, and textual analysis with temporal grounding. The system achieves 30% improvement over existing methods in target identification while providing interpretable, actionable evidence for human moderators rather than functioning as a black box.

AINeutralarXiv – CS AI · Apr 136/10

🧠

Detection of Hate and Threat in Digital Forensics: A Case-Driven Multimodal Approach

Researchers present a forensic-focused multimodal framework for detecting hate speech and threats across images, documents, and text. The approach intelligently determines what evidence is present before applying appropriate AI models, improving accuracy and evidentiary traceability in digital investigations.