# From Hidden to Harmful: Connecting Implicit and Explicit Hate Through Implied Statements

Nicolás Benjamín Ocampo, Elena Cabrio, Serena Villata

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Nicolás Benjamín Ocampo, Elena Cabrio, Serena Villata. From Hidden to Harmful: Connecting Implicit and Explicit Hate Through Implied Statements. WI-IAT 2025 - 24th IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, Nov 2025, London, United Kingdom. hal-05453245

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# From Hidden to Harmful: Connecting Implicit and Explicit Hate Through Implied Statements

Nicolás Benjamín Ocampo*

Centrum Wiskunde & Informatica

Amsterdam, The Netherlands

n.b.ocampo@cwi.nl

Elena Cabrio

Université Côte d'Azur

Inria, CNRS, I3S

Sophia Antipolis, France

elena.cabrio@univ-cotedazur.fr

Serena Villata

Université Côte d'Azur

Inria, CNRS, I3S

Sophia Antipolis, France

serena.villata@univ-cotedazur.fr

**Abstract**—Implicit hate speech remains a major challenge in online content moderation. Unlike explicit hate, which can often be detected through direct cues by automated systems, implicit hate is subtle, context-dependent, and easily misclassified as non-hateful. Recent efforts have explored contrastive learning to improve the representation of implicit hate, but they still struggle with high misclassification rates. In this work, we introduce an approach that automatically extracts the Implied Statement (IS)—the hidden negative stereotype within an implicit hate message—and links implicit messages with their explicit counterparts through their shared IS. Additionally, we refine model training by using negative pairs, forcing implicit hate to be distinguished from non-hateful content. Our results show that connecting explicit and implicit hateful messages through their IS reduces false negatives, and separating implicit hate from non-hateful messages reduces false positives, leading to stronger, more accurate representations, and outperforming existing state-of-the-art methods in implicit hate detection, by 2 to 4% in macro F1-score.

**Index Terms**—hate speech detection, implicitness, contrastive learning, implied statement

**Content warning:** This paper contains unobfuscated examples some readers may find offensive.

## I. INTRODUCTION

Detecting Hate Speech (HS) is crucial for maintaining healthy online spaces, yet the problem is far from being solved. While explicit hate speech is relatively easy to identify automatically, implicit hate—messages with covert meanings, ambiguous, or indirect language that does not immediately denote hate but still disparages a person or a target group [1], [2]—remains a major challenge. Because these messages are subtle, even state-of-the-art models struggle to detect them, leading to high false negative and positive rates [3], [4].

Recent research has started tackling implicit hate speech using contrastive learning, a method that refines the model's training objective to ensure that similar messages (despite being encoded differently by models) are brought closer in representation space, and dissimilar messages (despite being encoded similarly by models) are pushed further apart in representation space [5]–[7]. While these approaches have shown promising advances, they lack *i*) a clear connection

<figure><img src="image_2.png" /><figcaption>Fig. 1. Implicit, Explicit, and Implied Statement examples sharing the same underlying meaning.</figcaption></figure>

between implicit and explicit hate through their implied negative stereotype, failing to capture their connection with possible explicit counterparts; and *ii*) an appropriate embedding representation for implicit hateful messages, that are encoded similarly to non-hateful ones (given that they do not contain explicit hateful words), leading to often misclassifying implicit hate messages as non-hateful.

From implicit messages, we can derive their implicit negative stereotypes, also known as Implied Statements (IS) [8]. For example, Figure 1 shows an IS extracted from an implicit hateful message. In the example, both the implicit and the explicit hate messages share the same IS. From this example, we can intuit that implicit messages should have representations closer to explicit hate messages in the embedding space rather than non-hateful ones. Therefore, in this work, we hypothesize that if an implicit message currently mis-encoded as non hateful by state-of-the-art approaches (e.g., [5], [6], [9], [10]) shares a similar representation with an explicit message conveying the same IS, we could use this alignment to detect such implicit message and reduce false negatives. Moreover, we hypothesize that increasing the separation between implicit hate speech and non-hateful messages in the embedding space could help lower the number of false positives.

In this work, we propose a novel approach to fine-tune a pretrained language model using contrastive learning, in a setting specifically designed to link explicit and implicit messages in the representation space (§III) to address the task of hate speech detection. Unlike [6], who pair explicit and implicit HS messages based solely on shared target groups, our approach

<footnote>*This research was conducted while employed by Université Côte d'Azur, INRIA, CNRS, I3S.</footnote>
ensures that explicit and implicit messages conveying the same
underlying meaning—by sharing the same IS—are paired
together and therefore enforced to have a similar embedding
representation through contrastive learning. To obtain IS for
implicit hate speech messages, we automatically generate them
using a language model in contrast to [5] that depends on
manually created IS.

We test our approach on the five most relevant datasets specifically built as benchmarks to target implicit hate speech detection (§IV): Social Bias Inference Corpus (SBIC) [11], DynaHate (DYNA) [12], Implicit Hate Corpus (IHC) [1], ToxiGen (TOX) [13], and ISHate [2]. Finally, we conduct an extensive cross-evaluation on the task of hate speech detection (HS vs Non-HS) across the five datasets (§V), comparing the obtained results with their vanilla counterpart and detection systems from related work. We show that by explicitly modeling the connection between implicit and explicit hate, we can significantly improve detection performance, outperforming existing state-of-the-art methods in hate speech detection.

## II. RELATED WORK

HS detection has been extensively studied by the natural
language processing (NLP) research community providing
multiple resources, such as lexicons [14], [15], datasets [16]–
[19], and supervised methods [20]–[23] (for a survey, see
[24], [25]). However, the problem is far from being solved, as
hate speech can be delivered in different implicit and subtle
ways [1], [2], [4]. For example, Elsherief et al. [1] identify 8
challenging taxonomies of Implicit HS and encourage the re-
search community to further explore these categories. Ocampo
et al. [2] show that 18 implicit properties are recognized
in implicit messages from the ISHate dataset. In particular,
the most prevalent ones are Inference (58.885%), Context
(48.627%), Sentiment (45.961%), Exaggeration (28.988%),
and Irony (22.213%). They also show how transformer models
fail to detect these notions. Similarly, Zhang et al. [4] show
how Large Language Models (LLMs) are poorly calibrated
when detecting implicit hate speech, as they tend to focus
on sensitive groups. In addition, theoretical analyses [3], [26],
[27] have been proposed to discuss why current systems cannot
properly detect implicitness.

### A. Implicit Hate Benchmarks

Recently, novel benchmark datasets designed mainly to
distinguish and detect implicit HS have been released. SBIC,
DYNA, IHC, TOX and ISHate (introduced in Section I) are
among the most relevant ones. IHC and ISHate analyzed
implicit hate by manually annotating these concepts in coarse-
grained (Explicit HS, Implicit HS, Non-HS) and fine-grained
labels (taxonomies and implicit properties). SBIC collected HS
messages containing an implicit negative stereotype. DYNA
consisted of adversarial hateful examples crafted through
crowd-sourcing. Finally, TOX generated implicit HS messages
using LLMs and constrained beam search. Each resource
constitutes a challenging test-bed to evaluate computational
approaches addressing implicit HS detection.

### B. Implicit Hate Detection

Among the contributions to implicit HS detection, Han and
Tsvetkov [28] develop a veiled toxicity detector system that,
from a classifier of 1% accuracy on implicit hate, improves it
up to 51% while preserving overt hate. Jaremko et al. [29] and
Wiegand and Ruppenhofer [30] make a deep analysis of how
current LLMs perform when detecting implicit HS and when
target identity groups are depicted as deviating from the norm.
Ghosh et al. [31] present CoSyn, a context synergized neural
network that employs user and conversational context to detect
implicitness with improvements in the range of 1.24% - 57.8%.
Yang et al. [32], Huang et al. [33], and Damo et al. [34]–[36]
engage with LLMs to detect and generate explanations for im-
plicit HS, either as full sentences or explanation chains. Also,
different guided augmentation strategies have been proposed
to refine classifiers in detecting veiled HS [37]–[40].

Other systems focus on properly representing implicit mes-
sages by enhancing HS generalization through contrastive
learning. Contrastive learning methods have been lately rec-
ognized as an effective approach for detecting implicit hate
speech due to their ability to handle and embed subtle differ-
ences and similarities between hateful and non-hateful content.
In this direction, Kim et al. [5] tackle cross-dataset under-
performing issues on HS classifiers and propose a contrastive
learning method that encodes a hateful post and its correspond-
ing implication close in representation space. Ocampo et al.
[6] use contrastive learning to link explicit and implicit hate
messages based on their target groups, showing improvements
in cross-evaluation settings. Ahn et al. [7] propose ShareCon, a
clustering-based contrastive learning approach using K-means
where clustered messages are considered as positive pairs and
are forced to be closer in representation space. Jiang et al. [10]
proposes a causal contrastive learning training method which,
given a detector, mines hard positive examples based on both
ground truth labels and predicted labels and anchors them to
reduce false negatives.

Following these previous works, we propose a novel ap-
proach for the task of implicit hate speech detection (cast
as the standard binary classification task: HS vs Non-HS)
using a contrastive learning approach. None of the previous
works on contrastive learning have focused on the creation
of negative pairs and positive pairs using explicit and implicit
labels. Ocampo et al. [6] experiment in that direction, but they
align explicit and implicit messages solely through the target
of hate without considering the underlying meaning between
the built pairs. We overcome this limitation by linking through
the shared IS. Our work also differs from [7] that rely on
clustering to avoid using manually annotated IS, such as [5].
Manually annotating the IS is an expensive task (as highlighted
by [7], [8]) and we propose to address it by automatically
generating them using a fine-tuned IS generator from [8].

## III. METHODOLOGY

Our work addresses the task of detecting hate speech,
focusing in particular on the challenges raised by implicit
hate speech detection. Given a dataset containing implicit HS,
<figure><img src="image_3.png" /><figcaption>Fig. 2. Overview of our approach for implicit hate speech detection. Given a dataset with implicit HS, explicit HS, and non-HS messages, the method involves three key steps: (i) generating IS and embedding them in a shared representation space, (ii) constructing contrastive learning pairs to refine message representations, and (iii) fine-tuning a pre-trained model using contrastive and cross-entropy loss to optimize classification performance.</figcaption></figure>

explicit HS, and non-HS messages, the novelty of our approach lies in: i) generating the IS for implicit HS messages and building positive pairs by matching implicit HS messages with explicit HS messages that share the same IS, and negative pairs by matching implicit HS messages with non-HS messages, ii) fine-tuning a pre-trained model using contrastive learning on the task of implicit HS detection. Positive pairs are intended to be close in the representation space, while negative pairs are encouraged to be distant. Our approach is illustrated in Figure 2.

### A. Implied Negative Stereotype Generation

An IS represents the underlying negative stereotype in an implicit hate speech message [1], [11], [41] (see example in Figure 1). We follow the approach of [8], [41] to extract and generate IS using a predefined subject - predicate - object structure (e.g., “Muslims are terrorists”). Specifically, we use a fine-tuned BART model in the HS → IS configuration with greedy search decoding (best performing generator described in [8]), repetition penalty=2, max_len=156, and temperature=1. The data used for fine-tuning is a combined dataset of SBIC and IHC, specifically including only those messages that have an annotated IS.

### B. Linking Explicit and Implicit Hate

Contrastive learning requires creating pairs of positive and negative samples to train a model using a modified loss function [42]. Positive samples are pairs of messages that should be closer in the representation space, while negative samples are pairs that should be pushed further apart.

The model we use is an encoder that maps a message t to a representation r = Encoder(t), where r ∈ RD and D is the dimension size. A classification layer then maps r to a binary label y ∈ {0.1} being 0 for Non-HS and 1 for HS.

**Positive pairs.** For each implicit message x, we compute its IS xIS. We then match x with an explicit message w that attacks the same protected group as x, such that the cosine distance between Embed(w) and Embed(xIS) is lower than a threshold δ, where Embed is an embedding function to measure sentence similarity. δ is a hyperparameter that

measures the tolerable difference distance between the IS and the explicit message.

**Negative pairs.** We pair each implicit HS message with a randomly selected non-hateful message. Contrastive learning then forces these two groups to be pushed further apart in the representation space. Since current models often confuse implicit hate speech with non-hateful messages, we leverage contrastive learning to strengthen the differences between these two groups.

Once positive and negative pairs are built, we define the contrastive loss as follows:

$$L_c = mean((1-l) \cdot s^2 + l \cdot max(0.m - s)^2)$$

Where l represents the label pair (1 for positive pairs, 0 for negative pairs), s is the cosine similarity between paired messages, and m is the margin hyper-parameter. For classification, the cross-entropy loss is:

$$L_e = - \sum_{i=0}^{N-1} (g_i \log(p_i) + (1 - g_i) \log(1 - p_i))$$

Where g is the gold label (labels of the dataset on which the model is fine-tuned) and p is the prediction. The final loss to optimize is $L = L_c + L_e$.

## IV. EXPERIMENTAL SETTINGS

In this section, we describe the datasets and models used for comparison and explain the reasons for their selection, as a basis for the evaluation presented in the next section.

### A. Datasets

We select the benchmarks SBIC, DYNA, IHC, TOX, and ISHate, because they have been built with the goal of being reference benchmarks for implicit hate speech detection, been widely used by previous research to test their approaches. We ensure that the definitions of HS are consistent across the datasets following the approach carried out in [6], [13], [35]. Specifically, for the SBIC dataset, messages are considered as HS if labeled as offensive and target a specific group. As for the explicit-implicit HS labeling across all datasets, the provided implicit labels are used for IHC and ISHate datasets.
<table><caption>TABLE I
COMPARING HS DATASETS. % HATE CLASS, % IMPLICIT, % EXPLICIT, AND % SUBTLE ARE THE PERCENT LABELED AS HATE, IMPLICIT HATE, EXPLICIT HATE, AND SUBTLE HATE, RESPECTIVELY.</caption><thead><tr><th>Dataset</th><th>Source</th><th>Size</th><th>% Hate</th><th>% Implicit</th><th>% Explicit</th><th>% Subtle</th></tr></thead><tbody><tr><td>IHC</td><td>Twitter</td><td>21480</td><td>38,124</td><td>86,702</td><td>13,298</td><td>-</td></tr><tr><td>SBIC</td><td>Social Media</td><td>147139</td><td>60,446</td><td>62,278</td><td>37,722</td><td>-</td></tr><tr><td>DYNA</td><td>Human-Machine</td><td>123432</td><td>53,896</td><td>58,065</td><td>41,935</td><td>-</td></tr><tr><td>ISHate</td><td>Social Media</td><td>63758</td><td>71,974</td><td>54,904</td><td>21,811</td><td>23,851</td></tr><tr><td>TOX</td><td>GPT-3</td><td>9900</td><td>42,657</td><td>45,489</td><td>54,511</td><td>-</td></tr></tbody></table>

<table><caption>TABLE II
DISTRIBUTION OF TARGET GROUPS ACROSS DATASETS.</caption><thead><tr><th>sanitized_target</th><th>DYNA</th><th>IHC</th><th>ISHate</th><th>SBIC</th><th>TOX</th></tr></thead><tbody><tr><td>Economic Status</td><td>2433</td><td>253</td><td>546</td><td>2080</td><td>54</td></tr><tr><td>Ethnic/Racial Minorities</td><td>30474</td><td>7924</td><td>15270</td><td>30378</td><td>3057</td></tr><tr><td>Immigrants and Refugees</td><td>8271</td><td>1524</td><td>10690</td><td>1505</td><td>319</td></tr><tr><td>LGBTQ+</td><td>16937</td><td>525</td><td>2010</td><td>7298</td><td>803</td></tr><tr><td>Misc. Groups</td><td>1795</td><td>58</td><td>328</td><td>2529</td><td>8</td></tr><tr><td>Nationalities/Regions</td><td>6099</td><td>1109</td><td>3631</td><td>5034</td><td>1246</td></tr><tr><td>Non-Spec./Gen. Targets</td><td>12963</td><td>1713</td><td>7395</td><td>31148</td><td>422</td></tr><tr><td>People with Disabilities</td><td>4287</td><td>128</td><td>1235</td><td>7976</td><td>1377</td></tr><tr><td>Political Affiliations</td><td>5408</td><td>4116</td><td>3114</td><td>8231</td><td>80</td></tr><tr><td>Professions and Roles</td><td>2219</td><td>138</td><td>499</td><td>3667</td><td>42</td></tr><tr><td>Religious Groups</td><td>14631</td><td>3288</td><td>12754</td><td>12460</td><td>1511</td></tr><tr><td>Women</td><td>17915</td><td>704</td><td>6286</td><td>34833</td><td>981</td></tr></tbody></table>

For SBIC, DYNA, and TOX, we compute the percentage of HS implicit messages as the ones where none of the words of the Google profanity words resource is present*. Also, for the annotations of the target of hate, each dataset uses a different annotation approach. Therefore, we rename these targets and use homogeneous labeling for each benchmark following the experimental settings of [6], [35], that perform a similar data preprocessing. The datasets are divided into training, development, and test sets. For datasets with existing splits, we retain the original partitions and ensure no data contamination with the splits used by the BART IS generator. Datasets without predefined splits are partitioned using a stratified sampling method, following a 60% training, 20% development, and 20% test ratio.

## B. Models

We evaluate our system on the task of detecting implicit hate speech, framed as binary classification task where the objective is to correctly detect whether a text message is considered hateful or not (on datasets with a high percentage of implicit HS data). We integrate our contrastive learning approach on BERT [43] and HateBERT [44], two widely used pretrained language models. BERT is a general-purpose language model, while HateBERT is a domain-adapted version pre-trained on abusive language, making it particularly suited for hate speech detection. We call the contrastive learning versions of BERT and HateBERT, ISContBERT and ISContHateBERT, respectively.

We compare our approach against vanilla baselines (fine-tuning BERT and HateBERT without contrastive learning) and contrastive learning methods previously explored in the literature:

Kim et al. [5] – A contrastive method that brings hateful posts and their implications closer in the representation space to improve cross-dataset performance.

Ocampo et al. [6] – A contrastive approach linking explicit and implicit hate messages based on shared target groups.

Ahn et al. [7] – A clustering-based contrastive learning method using K-means, where clustered messages form positive pairs.

Jiang [10] – A causal contrastive learning model that mines hard positive examples based on both ground-truth and predicted labels.

We train our model configurations exclusively on the SBIC and IHC datasets because the BART model from [8] is originally trained on these two corpora. As a first step, we use this model to replicate and validate their findings, and to evaluate BART’s ability to generate IS as a way to validate our own approach. Our preliminary results align with those in the original study.

After that, we leverage the IS generated by BART on the SBIC and IHC datasets, in combination with contrastive learning, to fine-tune ISContBERT and IS-ContHateBERT. To build positive and negative pairs for these two models, IS representations are embed using the function `Embed=Sentence-BERT` [45], specifically the `all-MiniLM-L6-v2` version. For the $\delta$ parameter, we conducted preliminary experiments and manual analysis to optimize the generation of positive pairs. We selected a value of $1 \times 10^{-1}$ as it demonstrated proper alignment between the implicit, IS, and explicit messages.

For the rest of the parameters we use a batch size of 4, a learning rate of 2e-5, and train for 4 epochs. We set 5 different random seeds and evaluate performance using F1-score (`mainMetric=f1`) with a classification threshold of 0.5. Inputs are tokenized to a maximum sequence length of 512, and for contrastive learning, we use a margin of $m=0.5$. Both BERT and HateBERT models are initialized from the `bert-base-uncased` version.

## V. RESULTS

Table III presents cross-evaluation results for all our model configurations across the five selected benchmark datasets on

<footnote>*List of swear words banned by Google: https://github.com/RobertJGabriel/Google-profanity-words.</footnote>
<table><caption>TABLE III
CROSS-EVALUATION RESULTS WITH FIVE DIFFERENT RUN SEEDS. THE METHODS WE PROPOSE ARE UNDERLINED.</caption><thead><tr><th rowspan="2">Train</th><th rowspan="2">Model</th><th colspan="5">Test (Macro F1)</th></tr><tr><th>IHC</th><th>SBIC</th><th>DynaHate</th><th>ISHate</th><th>ToxiGen</th></tr></thead><tbody><tr><td rowspan="7">IHC</td><td>Kim et al. (2022)</td><td>0.7210<sup>± 0.0123</sup></td><td>0.6425<sup>± 0.0152</sup></td><td>0.5231<sup>± 0.0115</sup></td><td>0.6507<sup>± 0.0098</sup></td><td>0.5629<sup>± 0.0171</sup></td></tr><tr><td>Ocampo et al. (2023b)</td><td>0.7315<sup>± 0.0098</sup></td><td>0.6520<sup>± 0.0139</sup></td><td>0.5338<sup>± 0.0103</sup></td><td>0.6571<sup>± 0.0076</sup></td><td>0.5712<sup>± 0.0162</sup></td></tr><tr><td>Ahn et al. (2024)</td><td>0.7358<sup>± 0.0111</sup></td><td>0.6562<sup>± 0.0145</sup></td><td>0.5389<sup>± 0.0097</sup></td><td>0.6633<sup>± 0.0083</sup></td><td>0.5768<sup>± 0.0183</sup></td></tr><tr><td>Jiang (2025)</td><td>0.7402<sup>± 0.0105</sup></td><td>0.6601<sup>± 0.0137</sup></td><td>0.5455<sup>± 0.0089</sup></td><td>0.6692<sup>± 0.0069</sup></td><td>0.5810<sup>± 0.0159</sup></td></tr><tr><td>BERT</td><td>0.7418<sup>± 0.0066</sup></td><td>0.6877<sup>± 0.0157</sup></td><td>0.5463<sup>± 0.0183</sup></td><td>0.6748<sup>± 0.0117</sup></td><td><strong>0.6141<sup>± 0.0100</sup></strong></td></tr><tr><td>HateBERT</td><td>0.7433<sup>± 0.0107</sup></td><td>0.6618<sup>± 0.0173</sup></td><td>0.5415<sup>± 0.0092</sup></td><td>0.6678<sup>± 0.0058</sup></td><td>0.5843<sup>± 0.0153</sup></td></tr><tr><td><u>ISContBERT</u></td><td><strong>0.7625<sup>± 0.0063</sup></strong></td><td><strong>0.6891<sup>± 0.0072</sup></strong></td><td>0.5511<sup>± 0.0077</sup></td><td>0.6824<sup>± 0.0068</sup></td><td>0.6074<sup>± 0.0329</sup></td></tr><tr><td><u>ISContHateBERT</u></td><td>0.7618<sup>± 0.0027</sup></td><td>0.6824<sup>± 0.0129</sup></td><td><strong>0.5679<sup>± 0.0522</sup></strong></td><td><strong>0.6959<sup>± 0.0238</sup></strong></td><td>0.5896<sup>± 0.0119</sup></td></tr><tr><td rowspan="7">SBIC</td><td>Kim et al. (2022)</td><td>0.6402<sup>± 0.0135</sup></td><td>0.8023<sup>± 0.0121</sup></td><td>0.6105<sup>± 0.0137</sup></td><td>0.6993<sup>± 0.0142</sup></td><td>0.6351<sup>± 0.0195</sup></td></tr><tr><td>Ocampo et al. (2023b)</td><td>0.6508<sup>± 0.0129</sup></td><td>0.8104<sup>± 0.0115</sup></td><td>0.6187<sup>± 0.0125</sup></td><td>0.7056<sup>± 0.0135</sup></td><td>0.6403<sup>± 0.0189</sup></td></tr><tr><td>Ahn et al. (2024)</td><td>0.6554<sup>± 0.0118</sup></td><td>0.8159<sup>± 0.0109</sup></td><td>0.6233<sup>± 0.0119</sup></td><td>0.7105<sup>± 0.0126</sup></td><td>0.6468<sup>± 0.0182</sup></td></tr><tr><td>Jiang (2025)</td><td>0.6619<sup>± 0.0102</sup></td><td>0.8225<sup>± 0.0098</sup></td><td>0.6290<sup>± 0.0105</sup></td><td>0.7158<sup>± 0.0113</sup></td><td>0.6530<sup>± 0.0175</sup></td></tr><tr><td>BERT</td><td><strong>0.6630<sup>± 0.0049</sup></strong></td><td>0.8602<sup>± 0.0034</sup></td><td>0.6490<sup>± 0.0056</sup></td><td>0.7493<sup>± 0.0093</sup></td><td>0.6574<sup>± 0.0145</sup></td></tr><tr><td>HateBERT</td><td>0.6593<sup>± 0.0095</sup></td><td>0.8620<sup>± 0.0064</sup></td><td><strong>0.6591<sup>± 0.0069</sup></strong></td><td><strong>0.7583<sup>± 0.0086</sup></strong></td><td>0.6742<sup>± 0.0208</sup></td></tr><tr><td><u>ISContBERT</u></td><td>0.6603<sup>± 0.0301</sup></td><td>0.8568<sup>± 0.0092</sup></td><td>0.6500<sup>± 0.0310</sup></td><td>0.7581<sup>± 0.0167</sup></td><td>0.6939<sup>± 0.0086</sup></td></tr><tr><td><u>ISContHateBERT</u></td><td>0.6385<sup>± 0.0310</sup></td><td><strong>0.8632<sup>± 0.0099</sup></strong></td><td>0.6355<sup>± 0.0399</sup></td><td>0.7522<sup>± 0.0153</sup></td><td><strong>0.6998<sup>± 0.0086</sup></strong></td></tr></tbody></table>

<table><caption>TABLE IV
CROSS-EVALUATION RESULTS (MACRO ACCURACY) BETWEEN BERT/HATEBERT AND ISCONTBERT/ISCONTHATEBERT, REPORTED PER LABEL OVER 5 DIFFERENT RUN SEEDS. RESULTS FOR BERT/ISCONTBERT APPEAR IN THE UPPER ROWS, AND FOR HATEBERT/ISCONTHATEBERT IN THE LOWER ROWS. EXP, IMP, AND N-HS DENOTE EXPLICIT HATE SPEECH, IMPLICIT HATE SPEECH, AND NON-HATE SPEECH, RESPECTIVELY.</caption><thead><tr><th rowspan="3">Train</th><th colspan="15">Test (Macro Accuracy)</th></tr><tr><th colspan="3">IHC</th><th colspan="3">SBIC</th><th colspan="3">DynaHate</th><th colspan="3">ISHate</th><th colspan="3">ToxiGen</th></tr><tr><th>Exp</th><th>Imp</th><th>N-HS</th><th>Exp</th><th>Imp</th><th>N-HS</th><th>Exp</th><th>Imp</th><th>N-HS</th><th>Exp</th><th>Imp</th><th>N-HS</th><th>Exp</th><th>Imp</th><th>N-HS</th></tr></thead><tbody><tr><td>IHC BERT</td><td>0.8027</td><td>0.6766</td><td>0.7931</td><td>0.8754</td><td>0.8678</td><td><strong>0.5206</strong></td><td>0.7054</td><td>0.8044</td><td><strong>0.3525</strong></td><td>0.8153</td><td>0.7462</td><td><strong>0.5933</strong></td><td>0.7560</td><td>0.7384</td><td><strong>0.5289</strong></td></tr><tr><td>IHC ISContBERT</td><td><strong>0.8345</strong></td><td><strong>0.6959</strong></td><td><strong>0.8121</strong></td><td><strong>0.9053</strong></td><td><strong>0.9023</strong></td><td>0.4963</td><td><strong>0.7332</strong></td><td><strong>0.8363</strong></td><td>0.3368</td><td><strong>0.8437</strong></td><td><strong>0.7495</strong></td><td>0.5884</td><td><strong>0.7835</strong></td><td><strong>0.7654</strong></td><td>0.5116</td></tr><tr><td>SBIC BERT</td><td>0.6770</td><td>0.5514</td><td><strong>0.7566</strong></td><td>0.8920</td><td>0.8348</td><td><strong>0.8607</strong></td><td>0.6583</td><td>0.6675</td><td><strong>0.6367</strong></td><td>0.6457</td><td>0.5935</td><td><strong>0.8489</strong></td><td>0.6374</td><td>0.6789</td><td><strong>0.6701</strong></td></tr><tr><td>SBIC ISContBERT</td><td><strong>0.7469</strong></td><td><strong>0.6010</strong></td><td>0.7139</td><td><strong>0.9214</strong></td><td><strong>0.8766</strong></td><td>0.8031</td><td><strong>0.7156</strong></td><td><strong>0.7379</strong></td><td>0.5790</td><td><strong>0.7081</strong></td><td><strong>0.6151</strong></td><td>0.8194</td><td><strong>0.6945</strong></td><td><strong>0.7308</strong></td><td>0.6673</td></tr><tr><td>IHC HateBERT</td><td>0.7938</td><td>0.6599</td><td>0.8087</td><td>0.8832</td><td>0.8842</td><td>0.4659</td><td>0.6855</td><td>0.8184</td><td><strong>0.3447</strong></td><td>0.8314</td><td>0.7355</td><td>0.5728</td><td><strong>0.8143</strong></td><td><strong>0.7784</strong></td><td><strong>0.4493</strong></td></tr><tr><td>IHC ISContHateBERT</td><td><strong>0.8195</strong></td><td><strong>0.6802</strong></td><td><strong>0.8250</strong></td><td><strong>0.9062</strong></td><td><strong>0.8997</strong></td><td><strong>0.4669</strong></td><td><strong>0.7203</strong></td><td><strong>0.8626</strong></td><td>0.3203</td><td><strong>0.8556</strong></td><td><strong>0.7559</strong></td><td><strong>0.5792</strong></td><td>0.8132</td><td>0.7741</td><td>0.4489</td></tr><tr><td>SBIC HateBERT</td><td>0.6584</td><td>0.4929</td><td><strong>0.7980</strong></td><td>0.8971</td><td>0.8440</td><td><strong>0.8568</strong></td><td>0.6566</td><td>0.6592</td><td><strong>0.6638</strong></td><td>0.6598</td><td>0.5581</td><td><strong>0.8574</strong></td><td>0.6670</td><td>0.6616</td><td><strong>0.6915</strong></td></tr><tr><td>SBIC ISContHateBERT</td><td><strong>0.7912</strong></td><td><strong>0.6231</strong></td><td>0.6635</td><td><strong>0.9420</strong></td><td><strong>0.9053</strong></td><td>0.7551</td><td><strong>0.7402</strong></td><td><strong>0.7643</strong></td><td>0.5330</td><td><strong>0.7546</strong></td><td><strong>0.6516</strong></td><td>0.7760</td><td><strong>0.7396</strong></td><td><strong>0.7459</strong></td><td>0.6592</td></tr></tbody></table>

the two-label classification task (HS vs Non-HS). Results are reported in Macro F1-score, along with standard deviations computed over the five different run seeds. Our contrastive learning models (ISContBERT and ISContHateBERT), when trained on IHC, consistently outperform their standard counterparts (BERT and HateBERT) across all datasets except TOX. We hypothesize that this may be due to the fact that TOX is a dataset specifically created for adversarial hate speech, making it difficult to generate implied statements for TOX and therefore affect contrastive learning. When trained on SBIC, ISContHateBERT achieves superior results on TOX and SBIC, while ISContBERT delivers performance comparable to the best models on IHC, and ISHate. Underperformance of our method can be seen on Dynahate. Similarly to TOX, DYNA is a manually curated dataset specifically designed to fool detectors through adversarial examples, which can also affect the IS generation step.

Moreover, ISContBERT and ISContHateBERT surpasses

existing contrastive learning approaches, including [5]–[7], [10], on almost all the datasets when trained on both IHC, and SBIC. Our method only underperforms with previous existing methods when using ISContHateBERT evaluated on IHC and trained on SBIC. This results suggests our method produce state-of-the-art performance on hate speech detection (when comparing it with other contrastive learning methods) and better or comparable results when contrastive learning is not used on datasets specifically containing challenging implicit hateful messages.

Table IV provides insights into whether contrastive learning with IS affects the prediction of explicit HS, implicit HS, and Non-HS messages. The table shows the macro average accuracy on the test sets of the evaluated benchmarks. Results indicate that ISContBERT and ISContHateBERT consistently outperform their vanilla counterparts (BERT and HateBERT), particularly in detecting implicit and explicit hate speech.
<table><caption>TABLE V
CROSS-EVALUATION RESULTS COMPARING BERT/HATEBERT WITH CONTRASTIVELY TRAINED ISCONTBERT/ISCONTHATEBERT, REPORTING FALSE POSITIVE RATE (FPR) AND FALSE NEGATIVE RATE (FNR) ACROSS FIVE RUN SEEDS. RESULTS FOR BERT/ISCONTBERT ARE SHOWN IN THE UPPER ROWS, AND FOR HATEBERT/ISCONTHATEBERT IN THE LOWER ROWS.</caption><thead><tr><th rowspan="2">Train</th><th colspan="2">IHC</th><th colspan="2">SBIC</th><th colspan="2">DYNA</th><th colspan="2">ISHate</th><th colspan="2">TOX</th></tr><tr><th>FPR (%)</th><th>FNR (%)</th><th>FPR (%)</th><th>FNR (%)</th><th>FPR (%)</th><th>FNR (%)</th><th>FPR (%)</th><th>FNR (%)</th><th>FPR (%)</th><th>FNR (%)</th></tr></thead><tbody><tr><td>IHC BERT</td><td>20.6900</td><td>30.6002</td><td><strong>47.9400</strong></td><td>12.9103</td><td><strong>64.7500</strong></td><td>25.3085</td><td><strong>40.6700</strong></td><td>19.2319</td><td><strong>47.1100</strong></td><td>25.2723</td></tr><tr><td>IHC ISContBERT</td><td><strong>18.7900</strong></td><td><strong>28.4977</strong></td><td>50.3700</td><td><strong>9.6477</strong></td><td>66.3200</td><td><strong>22.3565</strong></td><td>41.1600</td><td><strong>16.6686</strong></td><td>48.8400</td><td><strong>22.5471</strong></td></tr><tr><td>SBIC BERT</td><td><strong>24.3400</strong></td><td>43.1271</td><td><strong>13.9300</strong></td><td>14.1888</td><td><strong>36.3300</strong></td><td>33.7842</td><td><strong>15.1100</strong></td><td>36.0055</td><td>32.9900</td><td>34.2031</td></tr><tr><td>SBIC ISContBERT</td><td>28.6100</td><td><strong>37.8870</strong></td><td>19.6900</td><td><strong>10.5142</strong></td><td>42.1000</td><td><strong>27.5049</strong></td><td>18.0600</td><td><strong>30.2154</strong></td><td><strong>33.2700</strong></td><td><strong>28.7509</strong></td></tr><tr><td>IHC HateBERT</td><td>19.1300</td><td>32.1625</td><td>53.4100</td><td>11.6208</td><td><strong>65.5300</strong></td><td>25.8769</td><td>42.7200</td><td>17.9173</td><td><strong>55.0700</strong></td><td><strong>20.3493</strong></td></tr><tr><td>IHC ISContHateBERT</td><td><strong>17.5000</strong></td><td><strong>30.0580</strong></td><td><strong>53.3100</strong></td><td><strong>9.7651</strong></td><td>67.9700</td><td><strong>22.0027</strong></td><td><strong>42.0800</strong></td><td><strong>15.5392</strong></td><td>55.1100</td><td>20.6179</td></tr><tr><td>SBIC HateBERT</td><td><strong>20.2000</strong></td><td>48.4265</td><td><strong>14.3200</strong></td><td>13.4359</td><td><strong>33.6200</strong></td><td>34.2310</td><td><strong>14.2600</strong></td><td>35.1413</td><td><strong>30.8500</strong></td><td>33.5676</td></tr><tr><td>SBIC ISContHateBERT</td><td>33.6500</td><td><strong>35.3707</strong></td><td>24.4900</td><td><strong>7.9743</strong></td><td>46.7000</td><td><strong>24.9694</strong></td><td>22.4000</td><td><strong>25.6756</strong></td><td>34.0800</td><td><strong>25.7278</strong></td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>Fig. 3. Comparison of embeddings of implicit, explicit, and implied messages in BERT and ISContBERT.</figcaption></figure>

## VI. DISCUSSION

**Stronger Detection of Explicit and Implicit Hate.** Across both BERT-based and HateBERT-based models, incorporating IS alignment and contrastive learning leads to significant improvements in classifying explicit and implicit hate speech. For instance, when trained and evaluated on IHC, ISContBERT improves explicit hate classification from 0.8027 to 0.8345 and implicit hate from 0.6766 to 0.6959. Similarly, ISContHateBERT increases implicit hate accuracy from 0.6599 to 0.6802 and explicit hate from 0.7938 to 0.8195. The trend is consistent across the other evaluation sets. When a model is better to correctly identify both Explicit and Implicit hate speech, it makes fewer errors where it misclassifies hate speech as non-hate speech. As a result, the number of False Negatives—instances where the model predicts Non-HS but the true label is either Explicit HS or Implicit HS—decreases. This effect can be seen as well through the False Negative Rate (FNR) shown in Table V.

**Linking between Explicit and Implicit messages.** Figure 3 shows the embedding spaces of implicit, explicit, and IS in two different models: BERT and ISContBERT. In BERT representation, the implicit message "Let mother nature evolve these people naturally because no amount of money or aid is going to drag these 'things' into the civilized world where

they serve absolutely no purpose." is positioned far from the explicit message "Black people don't have brains.", while the IS "Black people are uncivilized." nearby. However, after applying contrastive learning (ISContBERT), the embeddings become more compact bringing the implicit and explicit messages closer in the representation space.

**Effect of Margin parameter on ISContBERT.** We experimented with values of the parameter margin in the contrastive loss function. We retrained ISContBERT on the HS detection task using the IHC dataset but varying only this parameter. Margin weights the force of the distinction of negative pairs, in our case, Implicit HS and Non-HS messages. Figure 4 shows that the optimal performance is obtained with margin = 1.0 suggesting balance between positive and negative sample distinction.

**Increased Misclassification of Non-Hateful Messages.** While our models improve hate speech detection, some Non-HS messages are being misclassified. For example, Table IV shows that in SBIC, ISContBERT drops Non-HS accuracy from 0.5206 to 0.4963, when trained on IHC. This suggests that our models, by strongly associating implicit and explicit hate, may classify non-hs borderline cases as hateful. Table V shows that the False Positive Rate (FPR) for ISContHateBERT decreases when evaluated on IHC, increases on DYNA, and
<figure><img src="image_5.png" /><figcaption>Fig. 4. Effect of margin parameter on contrastive learning for the ISCont-BERT model trained and evaluated on IHC.</figcaption></figure>

remains relatively stable across the other benchmarks when trained on IHC. In contrast, ISContBERT shows a decrease in FPR only when tested on IHC, while in all other configurations, the FPR increases.

Overall, our results suggest that forcing an implicit message to align more closely with its explicit counterpart helps the model recognize underlying hateful intent, reducing false negatives. Specifically, we observe an approximate 3% reduction in False Negatives for both Explicit and Implicit hate speech messages across each dataset. However, our strategy impacts the performance on the Non-HS class and can keep, increase, or maintain its FPR. Best results are obtained when optimizing the separation between implicit hate speech messages and non-hate speech messages in the representation space at margin = 1).

## VII. CONCLUSION

Implicit hate speech remains one of the most challenging forms of harmful content to detect due to its subtle and indirect nature. Although recent contrastive learning approaches have improved models' ability to distinguish between hateful and non-hateful content, they still struggle to accurately recognize implicit hate [1], [2]. We hypothesize that bringing implicit and explicit hate speech representations closer together through contrastive learning could help the model better detect implicit hate and more effectively distinguish it from non-hateful speech.

In this work, we introduced a contrastive learning approach that explicitly connects implicit hate to its explicit counterpart through IS, capturing the underlying negative stereotype in an implicit message. Additionally, we enforced a stronger separation between implicit and non-hateful messages by leveraging negative pairs in the contrastive learning process. Our model has been extensively evaluated across five benchmark datasets for the task, and results show that our method outperforms the existing state-of-the-art approaches [5]–[7], [10], in the binary

task of hate speech detection (HS vs Non-HS), surpassing them by 2 to 4% in macro F1-score.

By structuring the representation space more effectively, our approach reduces false negatives by aligning implicit hate with explicit hate and lowers false positives by increasing the distinction between implicit hate and non-hate. This work not only improves detection accuracy but also provides a more interpretable way to understand implicit hate speech. Future research will explore further refinements in IS generation, adaptive margin selection, and the development of other suitable strategies for constructing positive and negative pairs in contrastive learning—based on dataset-specific features—moving beyond embedding-based matching of explicit and implicit HS.

## ACKNOWLEDGMENTS

This work has been supported by the French government, through the 3IA Cote d'Azur investments in the project managed by the National Research Agency (ANR) with the reference number ANR-23-IACL-0001. This work was also carried out with the support of ERCIM 'Alain Bensoussan' Fellowship Programme.

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