Organizations: Indian Institute of Information Technology Dharwad · Work done at FLaMe.nlp Lab, IIIT Delhi. · Manipal Institute of Technology, Manipal Academy of Higher Education Manipal, Karnataka, India · Indraprastha Institute of Information Technology Delhi
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.
Figures & tables
Typical Hateful Comment
– I dislike the skin colour of people from < country > .
– < country > is dishonest toward everyone; we should boycott it.
– < group/religion > people are ruining our society; they should leave.
Faux Hate Comments
– < country > is spreading COVID on purpose to harm the world.
– It’s not love, it’s a plan; < group/religion > use relationships to change people.
Table 1: Illustrative examples comparing Traditional Hate comments with Faux Hate comments. Placeholders such as COUNTRY and GROUP/RELIGION denote target entities abstractly.
Dataset
Hate
Fake
Explanation
Interdisciplinary
HateXplain Mathew et al. (2021)
✓
✗
✗
✗
HatEval Basile et al. (2019)
✓
✗
✗
✗
Implicit Hate ElSherief et al. (2021)
✓
✗
✓
✗
HARE Yang et al. (2023)
✓
✗
✓
✗
CoAID Cui and Lee (2020)
✗
✓
✗
✗
FNVE Chen et al. (2025)
✗
✓
✓
✗
Table 2: Comparison of existing datasets across four key dimensions: hate speech labeling, fake/misinformation labeling, structured/multi-dimensional explanatory annotations, and interdisciplinary scope. The interdisciplinary attribute denotes the integration of two distinct domains within a single dataset, allowing their interaction to be studied rather than treated separately. ✓ indicates the presence of a feature, while ✗ indicates its absence. Unlike prior resources, HateMirage incorporates all four dimensions, supporting richer analysis and interpretability.
Comment
Fake Claim Base
Target
Intent
Implication
The coronavirus is just a scam planned by [Political Party] and [Country] to fool us all.
"[Country] is Using Covid as a Biological Weapon"
[Political Party], [Country]
To link [Political Party] and [Country] to a conspiracy theory about COVID-19.
Could fuel public anger against [Political Party] and [Country].
[Religious Group] and [Political Party], with [Person] in the mix, spread COVID to ruin [City] and harm [Country].
"[Religious Group] and [Political Party] collaborated to spread COVID-19."
[Political Party], [Person], [Religious Group]
To accuse [Political Party], [Person], and [Religious Group] of intentionally worsening the pandemic.
Could stir up communal tensions and increase distrust in political and religious groups.
[Religious Group] men are trapping innocent [Community] women in fake love to convert them. This is how [Country] is being taken over slowly.
"Love Jihad: [Religious Group] men are systematically luring women from other communities to convert them."
[Religious Group]
To portray [Religious Group] as deceptive and predatory, reinforcing the fabricated ’Love Jihad’ narrative.
Could deepen mistrust between communities, normalize religious profiling, and justify hostility toward [Religious Group].
Table 3: Representative examples of Faux Hate comments illustrating how explanations are derived from comments rooted in fake or manipulative narratives. Each example decomposes the comment into its underlying fake claim base, the entities targeted, inferred author intent, and the broader social or emotional implications.
Field
Avg Tokens
Median Tokens
Min Tokens
Max Tokens
Avg Sent
Max Sent Len
Multi-Sent(%)
Total Tokens
Train (3,624 samples)
Comments
30.56
29
3
800
2.82
240
76.49
110741
Target
1.48
1
1
27
1.00
12
0.30
5338
Intent
14.66
14
5
39
1.02
34
1.41
53101
Implication
15.16
15
6
48
1.01
38
0.86
54878
Test (906 samples)
Comments
30.28
27
4
316
2.84
78
73.18
27438
Target
1.51
1
1
44
1.01
24
0.44
1371
Table 4: Quantitative summary of the HateMirage dataset, reporting key statistics for the complete dataset and its train and test splits. Metrics include text length, sentence structure, and multi-sentence prevalence, providing an overview of the linguistic complexity and scale of the dataset.
Model
Target
Intent
Implication
SBERT
R-L (F1)
SBERT
R-L (F1)
SBERT
R-L (F1)
LLaMA-3.2-1B-Instruct
Zero-shot
50.19
30.91
57.99
26.25
47.04
12.79
LLaMA-3.2-3B-Instruct
53.43
34.62
57.67
26.81
47.76
16.36
LLaMA-3.1-8B-Instruct
48.29
21.39
56.68
27.95
51.24
15.62
Qwen-2.5-1.5B
53.57
35.93
57.86
25.56
43.41
14.45
Qwen-2.5-3B-Instruct
52.91
36.13
59.96
19.06
53.09
13.75
Table 5: Comparison of LLM performance across Zero-Shot and RAG-based settings. Highest results are highlighted in bold , and second-best results are marked with † . (Metrics reported in %)
Comment
Ground Truth
Model Prediction
Target
During the time of Corona, you also actively spread information about the <organization> . Have some shame for you were metaphorically “hit with a shoe on your face.” Please report the news with a bit of discretion.
Media / journalist
<organization>
Hey Brother, <organization> is spreading the coronavirus here because you’re not paying attention to it.
<organization>
Brother
Intent
<@user> Were the people from the group in Markaz there to catch corona?
To question the motives of the <organization> gathering and imply reckless behavior.
The commenter is questioning the intentions of people in Markaz, suggesting they might be there to spread misinformation about the COVID-19 pandemic.
I still advocate calling it the “kung-flu”. And label anyone suspected of being infected as “Flu Kang”.
To use racially insensitive language to mock and stigmatize people, especially Asians, in relation to COVID-19.
To spread hateful and fake narratives about the flu.
Implication
<@user> Why.. Did they have corona too? Oh, so you all have been spreading this disease since then… We’ve been blaming the <organization> for no reason… This has been going on traditionally.
It implies that the criticism directed toward <organization> was unwarranted and highlights bias or unfair treatment.
The comment could be interpreted as promoting general negativity or unrest, possibly encouraging harmful behavior.
If <organization> is responsible, then Namaste Trump is more responsible. <person> had given a warning on February 11 regarding Corona. He was mocked then. Who will you hold responsible for Namaste Trump? Trump or our PM? #shameOnRupani
This criticism aims to highlight perceived double standards and questions leadership decisions in handling the COVID-19 situation.
The comment could be interpreted as drawing a general comparison between different parties, possibly implying blame or criticism.
Table 6: Error analysis showing model predictions for Target, Intent, and Implication. Placeholders such as <organization> and <user> are used to anonymize entities and generalize examples.
SBERT
R-L (F1)
Target
0.7479
0.6293
Intent
0.7020
0.3458
Implication
0.5780
0.2013
Table 7: Human evaluation results comparing GPT-4 generated explanations with human-written explanations for 500 randomly sampled records.
Figure 1: Comparison of average ratings from two annotators for GPT-4-generated explanations across Target, Intent, and Implication.
MOE KLINNS Lab, Xi’an Jiaotong University · The Hong Kong University of Science and Technology (Guangzhou) · School of Cyber Science and Engineering, Xi’an Jiaotong University +1