Source-linked AI summary
HateMirage: An Explainable Multi-Dimensional Dataset for Decoding Faux Hate and Subtle Online Abuse
Sai Kartheek Reddy Kasu, Shankar Biradar, Sunil Saumya, Md. Shad Akhtar
TL;DR
Subtle hate embedded in misinformation is difficult to detect and explain, while existing datasets underrepresent this intersection. HateMirage addresses the gap with 4,530 YouTube-derived comments annotated for Target, Intent, and Implication and benchmarks open-source models in zero-shot and RAG settings. The benchmark establishes initial baselines for structured explanation generation and reports variation across model architectures and grounding strategies.
Problem
Existing hate speech datasets emphasize overt toxicity, leaving subtle hate embedded in misleading narratives and its explanatory reasoning underrepresented.
Method
HateMirage constructs a 4,530-comment dataset from debunked claims and related YouTube discussions, annotates Target, Intent, and Implication, and benchmarks open-source models in zero-shot and RAG settings.
Results
Model performance varies across size, architecture, and grounding strategy, with Phi-3-128k-3B-Instruct leading several zero-shot Target and Intent metrics while Mistralv0.3-7B-Instruct leads Implication.
Takeaways & Limitations
HateMirage provides a benchmark for interpretable NLP by linking misinformation cues to hateful discourse and exposing the reasoning behind implicit harm.
Takeaways & Limitations
Explanations are GPT-4-generated with RAG, and human verification covered only a random subset of 500 records, approximately 11% of the dataset.
Abstract
from arXiv · showhide
Subtle and indirect hate speech remains an underexplored challenge in online safety research, particularly when harmful intent is embedded within misleading or manipulative narratives. Existing hate speech datasets primarily capture overt toxicity, underrepresenting the nuanced ways misinformation can incite or normalize hate. To address this gap, we present HateMirage, a novel dataset of Faux Hate comments designed to advance reasoning and explainability research on hate emerging from fake or distorted narratives. The dataset was constructed by identifying widely debunked misinformation claims from fact-checking sources and tracing related YouTube discussions, resulting in 4,530 user comments. Each comment is annotated along three interpretable dimensions: Target (who is affected), Intent (the underlying motivation or goal behind the comment), and Implication (its potential social impact). Unlike prior explainability datasets such as HateXplain and HARE, which offer token-level or single-dimensional reasoning, HateMirage introduces a multi-dimensional explanation framework that captures the interplay between misinformation, harm, and social consequence. We benchmark multiple open-source language models on HateMirage using ROUGE-L F1 and Sentence-BERT similarity to assess explanation coherence. Results suggest that explanation quality may depend more on pretraining diversity and reasoning-oriented data rather than on model scale alone. By coupling misinformation reasoning with harm attribution, HateMirage establishes a new benchmark for interpretable hate detection and responsible AI research.
1. Introduction
HateMirage addresses the difficulty of detecting and explaining subtle hate embedded in misinformation or deceptive narratives. It introduces a multi-dimensional dataset and benchmarks language models for structured explanation generation.
- Motivation: Subtle hate masked by humor, irony, or fabricated narratives remains difficult for both human annotators and automated systems.Such content often avoids explicit slurs or profanity, making its harmful intent ambiguous.
- Research gap: Prior hate speech datasets largely emphasize overt hate and surface-level toxicity, limiting interpretability for misinformation-driven harm.Existing resources do not adequately capture hate that is manufactured, fueled, or triggered by misinformation.
- Contribution: HateMirage contains 4,530 Faux Hate comments annotated across Target, Intent, and Implication.The dataset was built from widely debunked claims and related YouTube discussions.
- Benchmarking: The benchmark evaluates open-source language models from 1B to 8B parameters in zero-shot and RAG-based settings using Sentence-BERT similarity and ROUGE-L F1.The unified setup measures structured explanation generation across Target, Intent, and Implication.
- Significance: HateMirage links misinformation cues to hateful discourse, making implicit harm more explicit for interpretable NLP and context-aware moderation.This connection supports justifying predictions in trustworthy content moderation.
2. Literature Review
Existing resources typically study hate speech or misinformation separately and provide limited structured explanation. HateMirage combines both domains with Target, Intent, and Implication annotations for richer analysis of misinformation-driven hate.
- Motivation: The intersection of hate speech and misinformation remains underexplored despite its relevance to social media manipulation and social consequences.This intersection involves interactions among intent, target, and potential impact.
- Hate speech datasets: HateXplain provides token-level rationales for hateful text but does not explain the commenter’s intent or the implication conveyed.Its explanatory layer remains focused on identifying hateful portions of comments.
- Misinformation datasets: Misinformation datasets such as CoAID and FNVE lack representations of hate or harmful intent, while Hostile treats hate and fake content independently.These designs do not model hate arising from false narratives as an integrated phenomenon.
- HateMirage: HateMirage combines fake and hate labeling with structured explanations spanning target, intent, and implication.This design supports analysis across social, psychological, and linguistic perspectives.
3. Dataset Overview
HateMirage is constructed from verified misinformation claims and related YouTube comments, then labeled and explained through automated models with human validation. Its structured fields represent who is targeted, why, and with what potential social consequences.
- Collection of Debunked Fake Claims: The dataset begins with widely circulated claims collected from reputable fact-checking websites and verified as false or misleading.These claims provide the basis for subsequent comment collection and analysis.
- Comment Collection and Labeling: Comments were collected from international English news channels, including English and Hindi-English code-mixed content, and assigned binary Hate and Fake labels.International channels were selected to reduce bias associated with local news coverage.
- Faux Hate Selection: Faux Hate instances were selected as comments labeled fake = 1 and hate = 1, including material from FEUD and newly collected YouTube comments.Hindi-English code-mixed comments were translated into English using GPT-4.
- Structured Explanations: Structured explanations capture the Target, Intent, and potential Implication of each comment to move beyond binary classification.These dimensions represent the attacked entity, underlying motivation, and possible societal impact.
- Explanation Generation: GPT-4 with retrieval support was used to infer implicit targets, intentions, and broader implications from nuanced misinformation contexts.The approach relies on pretrained world knowledge and reasoning without task-specific fine-tuning.
- Quality Validation: Human validation checks factual consistency, semantic coherence, and linguistic clarity in sampled generated explanations.Reviewers manually inspect 10–15% of instances from each generation batch and regenerate inconsistent outputs.
- Quantitative Analysis: Over 75% of comments contain multiple sentences, while Intent and Implication annotations typically average 14–15 tokens.Target annotations are usually one or two tokens, illustrating the dataset’s varied explanatory granularity.
4. Benchmarking, Experimental Results and Analysis
HateMirage benchmarks open-source models for structured explanations of Faux Hate across Target, Intent, and Implication, using zero-shot and retrieval-augmented settings. Results show uneven performance across dimensions, with Implication posing the greatest reasoning challenge and model quality not determined by scale alone.
- Benchmark Setup: The benchmark evaluates open-source models from 1B to 8B parameters for structured Target, Intent, and Implication explanation generation.Models are compared in zero-shot and RAG-based settings using Sentence-BERT similarity and ROUGE-L F1.
- Zero-Shot Results: Phi-3-128k-3B-Instruct leads zero-shot Target performance with 65.55% SBERT similarity and 50.36% ROUGE-L F1.It also achieves the best zero-shot Intent ROUGE-L F1 at 29.52%.
- Zero-Shot Results: Mistral-v0.3-7B-Instruct leads zero-shot Implication performance with 55.64% SBERT similarity and 17.39% ROUGE-L F1.Implication is described as the most abstract and context-dependent explanation dimension.
- RAG Results: RAG preserves the overall performance trends while improving grounding-sensitive components, with Phi-3 achieving 62.03% Intent SBERT similarity.Phi-3 also records second-best RAG Target results: 63.65% SBERT similarity and 47.81% ROUGE-L F1.
- Interpretation: Phi-3’s strong performance may reflect synthetic and reasoning-focused pretraining, although GPT-4 stylistic alignment may partly reward phrasing overlap.The authors therefore caution that the observed advantage may combine interpretive capability with reference-style similarity.
- Error Analysis: Qualitative analysis finds surface-cue errors for Target, generic explanations for Intent, and broad or underspecified consequences for Implication.These patterns indicate overgeneralization and difficulty capturing pragmatic, context-dependent meaning.
- Human Evaluation: GPT-4 exceeded 90% similarity in 208 of 500 Target records, 7 Intent records, and no Implication records.Human evaluation similarly found lower agreement for Implication than for Target and Intent.
5. Conclusion and Future Work
HateMirage provides a dataset for studying Faux Hate through structured explanations of Target, Intent, and Implication. Future work will extend the benchmark to multimodal deceptive hateful discourse and develop evaluation methods beyond lexical similarity.
- Conclusion: HateMirage focuses on Faux Hate, where hateful narratives emerge indirectly through misinformation or deceptive framing.Its structured annotations capture Target, Intent, and Implication to expose implicit meaning missed by surface-level analysis.
- Conclusion: The dataset is intended to support research and moderation by making harmful reasoning more transparent.The contribution centers on rich explanatory annotations rather than hate classification alone.
- Future Work: Future work will add memes and visual narratives to study text-image interactions in deceptive hateful discourse across social platforms.The planned extension is explicitly multimodal.
- Future Work: Future evaluation work will explore faithfulness metrics and model-based evaluators beyond lexical similarity.These methods are intended to assess reasoning consistency more directly.
6. Limitations
HateMirage’s explanations are synthetic and only partially human-verified, while its YouTube-based English corpus limits how broadly the resource may generalize.
- The explanations were generated with GPT-4 and retrieval-augmented generation rather than written entirely by humans.This choice reduced human exposure to distressing content but may omit subtlety and emotional variability found in human interpretations.
- The abstract Implication dimension was hardest for the model to infer accurately against human-written gold standards.
- Because the corpus comes from YouTube comments on international English news channels, it may not represent Faux Hate across all platforms or vernaculars.
7. Ethics Statement
The dataset uses public, anonymized YouTube comments and frames explainable reasoning as a route toward more accountable moderation, while recognizing potential misuse.
- Only publicly accessible YouTube comments were collected, with personally identifiable information excluded through anonymization procedures.
- The work aims to support more transparent and accountable moderation by explaining why content is harmful.
- Structured explanations may help moderation teams, fact-checkers, and researchers understand harmful content and its relationship to misinformation.
- The dataset could be misused to craft more sophisticated hateful narratives or target vulnerable groups.
- To mitigate misuse, the authors restrict access, anonymize entities, and prohibit redistribution or generative use that could amplify harm.