# Causality Guided Disentanglement for Cross-Platform Hate Speech Detection arXiv: https://arxiv.org/abs/2308.02080 ยท HTML: https://mlsanity.com/paper/2308.02080/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security. - Builds on 24 of its 50 references that are in the map. - First posted on arXiv 3 Aug 2023. ## Placed in - LLM safety and security / [Safety alignment, refusal and guardrails](https://mlsanity.com/safety/safety-alignment-refusal-and-guardrails/) ## Builds on (most cited first, 24 of 24) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016) - [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019) - [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023) - [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023) - [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014) - [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020) - [Domain Generalization using Causal Matching](https://mlsanity.com/paper/2006.07500/) (2020) - [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020) - [ChatGPT: Jack of all trades, master of none](https://mlsanity.com/paper/2302.10724/) (2023) - [Invariance, Causality and Robustness](https://mlsanity.com/paper/1812.08233/) (2018) - [Challenges in Automated Debiasing for Toxic Language Detection](https://mlsanity.com/paper/2102.00086/) (2021) - [Measuring a hate speech spectrum with faceted Rasch item response theory and perspective-aware, explainable-by-design deep learning](https://mlsanity.com/paper/2009.10277/) (2020) - [Handling Bias in Toxic Speech Detection: A Survey](https://mlsanity.com/paper/2202.00126/) (2022) - [Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media](https://mlsanity.com/paper/1804.04257/) (2018) - [Spread of hate speech in online social media](https://mlsanity.com/paper/1812.01693/) (2018) - [Detecting Hate Speech with GPT-3](https://mlsanity.com/paper/2103.12407/) (2021) - [Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation](https://mlsanity.com/paper/2307.01680/) (2023) - [Domain Generalization -- A Causal Perspective](https://mlsanity.com/paper/2209.15177/) (2022) - [Deep Learning for Hate Speech Detection: A Comparative Study](https://mlsanity.com/paper/2202.09517/) (2022) - [AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection](https://mlsanity.com/paper/2212.10405/) (2022) - [SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations](https://mlsanity.com/paper/2011.10684/) (2020) - [PEACE: Cross-Platform Hate Speech Detection- A Causality-guided Framework](https://mlsanity.com/paper/2306.08804/) (2023) - [Exploring Platform Migration Patterns between Twitter and Mastodon: A User Behavior Study](https://mlsanity.com/paper/2305.09196/) (2023) ## Built on by (most cited first, 9 of 9) - [Content Moderation by LLM: From Accuracy to Legitimacy](https://mlsanity.com/paper/2409.03219/) (2024) - [Exploring Causal Learning through Graph Neural Networks: An In-depth Review](https://mlsanity.com/paper/2311.14994/) (2023) - [Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection](https://mlsanity.com/paper/2506.02350/) (2025) - [Hate Speech Detection with Generalizable Target-aware Fairness](https://mlsanity.com/paper/2406.00046/) (2024) - [Harnessing Artificial Intelligence to Combat Online Hate: Exploring the Challenges and Opportunities of Large Language Models in Hate Speech Detection](https://mlsanity.com/paper/2403.08035/) (2024) - [Cross-Platform Hate Speech Detection with Weakly Supervised Causal Disentanglement](https://mlsanity.com/paper/2404.11036/) (2024) - [Black Box Absorption: LLMs Undermining Innovative Ideas](https://mlsanity.com/paper/2510.20612/) (2025) - [CAMO: Causality-Guided Adversarial Multimodal Domain Generalization for Crisis Classification](https://mlsanity.com/paper/2512.08071/) (2025) - [Introducing CausalBench: A Flexible Benchmark Framework for Causal Analysis and Machine Learning](https://mlsanity.com/paper/2409.08419/) (2024) ## Abstract (arXiv) Social media platforms, despite their value in promoting open discourse, are often exploited to spread harmful content. Current deep learning and natural language processing models used for detecting this harmful content overly rely on domain-specific terms affecting their capabilities to adapt to generalizable hate speech detection. This is because they tend to focus too narrowly on particular linguistic signals or the use of certain categories of words. Another significant challenge arises when platforms lack high-quality annotated data for training, leading to a need for cross-platform models that can adapt to different distribution shifts. Our research introduces a cross-platform hate speech detection model capable of being trained on one platform's data and generalizing to multiple unseen platforms. To achieve good generalizability across platforms, one way is to disentangle the input representations into invariant and platform-dependent features. We also argue that learning causal relationships, which remain constant across diverse environments, can significantly aid in understanding invariant representations in hate speech. By disentangling input into platform-dependent features (useful for predicting hate targets) and platform-independent features (used to predict the presence of hate), we learn invariant representations resistant to distribution shifts. These features are then used to predict hate speech across unseen platforms. Our extensive experiments across four platforms highlight our model's enhanced efficacy compared to existing state-of-the-art methods in detecting generalized hate speech.