Unsupervised Embeddings with Graph Auto-Encoders for Multi-domain and Multilingual Hate Speech Detection (2022.lrec-1)
Copied to clipboard
| Challenge: | Hate speech detection is a challenging task, since hate messages are often expressed in subtle ways and with characteristics that may vary depending on the author. |
| Approach: | They propose an unsupervised approach to learn embeddings for hate speech detection using Graph Auto-Encoders (GAE) they represent texts as nodes of a graph and use transformer layer and convolutional layer to encode them in low-dimensional space. |
| Outcome: | The proposed method shows competitive results on small datasets. |
Similar Papers
Improving Hate Speech Detection with Deep Learning Ensembles (L18-1)
Copied to clipboard
| Challenge: | censorship is a potential risk when addressing these issues with automated text classification methods. |
| Approach: | They propose to use a neural network-based ensemble method to better classify hate speech using a publicly available embedding model and a popular sentiment dataset. |
| Outcome: | The proposed method improves by 5 points on a hate speech corpus from Twitter and a popular sentiment dataset. |
HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods to detect online hate speech depend heavily on labeled datasets for training, which results in poor detection performance of the hate speech class. |
| Approach: | They propose a deep generative reinforcement learning model which augments two commonly-used hate speech detection datasets with the HateGAN generated tweets. |
| Outcome: | The proposed model improves the detection performance of hate speech class regardless of the classifiers and datasets used in the detection task. |
HARALD: Augmenting Hate Speech Data Sets with Real Data (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Hate speech detection depends on the availability of variable labeled data. |
| Approach: | They propose a method that uses real unlabelled data from online platforms to augment existing models by harvesting and processing it. |
| Outcome: | The proposed approach improves the classification performance of hate speech classification models. |
Improving Hate Speech Detection by Fusing Textual and User Interaction Representations in Online Communities (2026.acl-industry)
Copied to clipboard
| Challenge: | Existing studies on toxic content in online communities are limited by the scarcity of data that align textual content with comprehensive social interactions. |
| Approach: | They propose a user-aware hate speech detection framework that effectively fuses textual semantics with social interaction representations to provide pragmatic context for disambiguation. |
| Outcome: | The proposed framework outperforms strong text-only baselines by over 3.6%, validating the critical role of social context in enhancing detection accuracy. |
A Turkish Hate Speech Dataset and Detection System (2022.lrec-1)
Copied to clipboard
| Challenge: | Davidson et al., 2017: hate speech is a discourse that targets a specific group based on race, gender, religion, sexual orientation, etc. |
| Approach: | They propose a machine learning system for automatic detection of hate speech in Turkish . they use a hate speech dataset and a dataset to collect tweets about immigrants . |
| Outcome: | The proposed system is able to detect hate speech in Turkish and annotate it using BERTurk. |
Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for detecting hate speech data are expensive and time-consuming . labeled data is expensive and difficult to collect, especially for low-resource languages . |
| Approach: | They propose a method that leverages nearest-neighbor retrieval to augment minimal labeled data in target language. |
| Outcome: | The proposed method outperforms existing models on eight languages and is highly data-efficient. |
A Weakly Supervised Classifier and Dataset of White Supremacist Language (2023.acl-short)
Copied to clipboard
| Challenge: | Existing studies on white supremacist language have focused on specific hateful ideologies, but little attention has been given to specific hate speech. |
| Approach: | They propose a weakly supervised classifier for detecting white supremacist language . they use large datasets of white supremacy domains paired with neutral and anti-racist data from similar domains to train the classifiers. |
| Outcome: | The proposed classifiers outperform previous studies on white supremacist classification on unseen datasets and find strong generalization performance for models with weakly annotated data. |
Hate-Speech and Offensive Language Detection in Roman Urdu (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing research on hate-speech and offensive language detection in social media content is mainly focused on the English language. |
| Approach: | They propose to use an annotated dataset to detect hate-speech and offensive language in social media content . they propose to transfer five existing embedding models to Roman Urdu to test their performance . |
| Outcome: | The proposed model outperforms existing methods on RUHSOLD dataset and train domain-specific embeddings on more than 4.7 million tweets. |
Large-Scale Hate Speech Detection with Cross-Domain Transfer (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing datasets for hate speech detection are limited due to the labor cost. |
| Approach: | They construct large-scale tweet datasets for hate speech detection in English and a low-resource language, Turkish, consisting of human-labeled 100k tweets per each. |
| Outcome: | The proposed datasets outperform conventional bag-of-words and neural models by at least 5% in English and 10% in Turkish for large-scale hate speech detection. |
SharedCon: Implicit Hate Speech Detection using Shared Semantics (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent studies suggest that classifying hateful posts in a binary manner may not address nuanced task of detecting implicit hate speech. |
| Approach: | They propose a contrastive learning approach that leverages shared semantics among data to detect implicit hate speech. |
| Outcome: | The proposed approach is based on a clustering-based contrastive learning approach with human-written implications or machine-generated augmented data. |