Explainable, Cross‑Platform, Multilingual BERT‑Based Framework

with Continual Learning for Real‑Time, Fine‑Grained Hate Speech

Detection and Filtering on Online Social Platforms

Malayaj Kumar

, Harshal Mahajan

, Christins

, S. Arun Prasath

, Allam Balaram

and Keerthana S.

Department of Computer Science and Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, Jhunjhunu,

Rajasthan, India

Department of Computer Engineering, Indira College of Engineering and Management, Pune, Maharashtra, India

Department of Social Work, St Claret College, Bangalore, Karnataka, India

Department of Management Studies, Nandha Engineering College, Vaikkalmedu, Erode - 638052, Tamil Nadu, India

Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad‑500043, Telangana, India

Department of ECE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Keywords: Explainable AI, Continual Learning, Multilingual Hate Detection, real‑time Filtering, Fine‑Grained

Classification.

Abstract: This model introduces a new, explainable BERT-based architecture for hate speech detection and filtering in

real time, in several social media platforms. Enjoying the advantages of continual learning and light-weight

transformer variants, the proposed method remains effective with the newly emerged slurs and dynamic

linguistic patterns, meanwhile, it can sustain low latency performance. Multilinguality and code‑mixing are

supported through proprietary tokenizers and regional adaptor modules, ensuring strong performance on

dialects and languages. For a greater level of transparency, associated explainable AI techniques e.g. SHAP,

LIME help in better understanding of classification decisions and make use of user‑history-aware components

to help disambiguate context‑sensitive expressions. A fine‑grained multi‑label classification orders multiple

hate classes, and dynamic hate lexicon extension is integrated to keep pace with offensive terminologies.

Extensive cross‑platform validation yields better generalization and fairness to new datasets.

1 INTRODUCTION

Identification of hate speech on social media

platforms poses an important challenge, because of its

negative role in spreading the unwanted content that

can have severe psychological and social effects.

Conventional hate speech detection approaches fails

to handle the dynamic nature of language, the

evolution of new slurs, regional dialects and context

sensitive texts. In addition, most existing models do

not support real-time processing and do not provide

proper explanation of their decisions, which hampers

the application of these models in practice. We

present a novel method by combining BERT-based

model with improved continual learning strategy,

multilingual feature and fine-grained classification

for accurate and efficient hate speech detection in a

variety of online services. Our model is made to be

able to evolve as new hate terms are discovered and

new language patterns are created on the internet, yet

maintain high accuracy and low latency. The system

provides transparency by explaining the reasons for

the detection using explainable AI techniques such as

SHAP or LIME. Besides, the code-mixed text

handling, real-time filtering, entering history-aware

filtering and user-history aware filtering are used for

developing the robust context sensitive model. This

paper provides a holistic answer to the increasing

necessity of scalable, interpretable and context-aware

hate speech detection systems.

1.1 Problem Statement

The appearance of hate speech on the Internet is a

challenge which endangers the privacy and the

freedom of a person as well as the freedom of

information and therefore many elements of tolerance

124

Kumar, M., Mahajan, H., Christins, , Prasath, S. A., Balaram, A. and S., K.

Explainable, Cross-Platform, Multilingual BERT-Based Framework with Continual Learning for Realâ

STime, Fineâ

SGrained Hate Speech Detection and Filtering on Online Social Platforms.

DOI: 10.5220/0013858600004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

124-130

ISBN: 978-989-758-777-1

of society. In spite of significant developments in

(NLP) and machine learning, current hate speech

detection models face important issues, such as the

dynamical nature of language, multilingual contexts,

and the variance of sarcasm, slang, and coded speech.

Moreover, many existing systems are not real-time,

lack accountability in decision-making, and suffer

from bias from inferring imbalanced datasets, or

incorrect context recognitions. These constraints limit

the feasibility of realizing automatic systems that are

able to identify harmful content, while preserving

fairness and accuracy, on different social media

platforms. There is thus an exigent demand for a

reliable, scalable and interpretable hate speech

detection system that can dynamically evolve to new

linguistic developments, support multiple languages

and dialects, and provide transparent and context-

aware classification decisions to moderators and

users. We intend to focus on these by suggesting a

BERT-based solution with characteristics such as

continual learning, multilingual aspects, fine-grained

classes’ dominant topic, and real-time filtration to

efficiently identify and tackle hate speech, combining

compliance with ethical considerations with regard to

fairness and transparency.

2 LITERATURE SURVEY

Lately, BERT-based models have found widespread

use in hate speech detection because of their

contextual language understanding and

generalization capabilities to down-stream tasks.

Caselli et al. (2021) provided HateBERT, an

adaptation of BERT trained on comments from

Reddit which performed well in abusive post

detection, but is only applicable to training on the

same platform as it’s highly focused on the domain.

Schneider et al. (2023) evaluated BERT models on

Parler and found it can be effective at detecting

offensive language on a niche social media platform

but fails to transfer to mainstream ones.

With respect to the linguistic diversity, Singhal

and Bedi (2024), Almaliki et al. (2022) employed

BERT for hate speech detection in Arabic and

Turkish however, both works identified difficulty in

including dialectal variations and low-resource

language settings 3. Similarly, Patil et al. (2022)

considered hate speech in the Marathi language using

regional BERT models, raising the requirement of

local datasets while demonstrating the limitation of

their generalizability.

Multilingual and code-mixed hate speech remains

an underexplored frontier. Hossain et al. (2021) and

Guragain et al. (2025) attempted to tackle this issue

using multilingual BERT (mBERT), but tokenization

inconsistencies and loss of semantic nuance in code-

mixed texts persisted. Meanwhile, Jahan et al. (2024)

performed a comparative study of data augmentation

techniques, underlining that many BERT-based

models suffer performance degradation when trained

on synthetically generated or low-quality data.

Explainability and transparency are also major

concerns. While Mathew et al. (2021) introduced

HateXplain to benchmark explainable models, most

existing solutions still operate as black boxes, failing

to provide justifications behind classifications.

Pendzel et al. (2023) highlighted this issue in their

evaluation of generative AI-based hate detectors,

stating that hallucinations and bias propagation limit

real-world adoption.

Another challenge arises in detecting implicit,

sarcastic, and context-sensitive hate speech. Chung et

al. (2022) and Wei et al. (2021) incorporated

emotion-aware attention layers, yet models struggled

to distinguish between satire and genuine hate

content, especially in shorter or ambiguous messages.

Class imbalance and annotation inconsistencies

also undermine current models’ reliability. Kaur and

Singh (2023) noted that biased datasets lead to high

false negatives for subtle hate forms, while

Mohammed et al. (2023) criticized the low inter-

annotator agreement in commonly used datasets.

Additionally, Rajput et al. (2021) and Zhang et al.

(2024) emphasized that while static and zero-shot

embeddings offer speed, they sacrifice depth of

understanding and domain specificity.

Recent developments have started exploring

ensemble models and hybrid architectures. Singh &

Chakraborty (2022) reported that ensembles

combining CNNs with BERT significantly boost

accuracy but at the cost of latency and computational

overhead. Finally, Gupta et al. (2022) and Roy et al.

(2023) demonstrated that cross-platform and user-

history–aware models can outperform isolated

solutions, yet few systems integrate these components

effectively.

3 METHODOLOGY

3.1 Multilingual & Multiplatform Data

Collection

The foundation of the proposed framework is built

upon a diverse, multilingual, and cross-platform

dataset:

• Sources: Twitter, Facebook, Reddit, YouTube.

Explainable, Cross-Platform, Multilingual BERT-Based Framework with Continual Learning for Realâ

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• Languages: English, Arabic, Marathi, Hindi-

English code-mixed.

• Scope: Over 100,000 annotated posts.

Datasets were manually and semi-automatically

labeled for hate and non-hate categories to ensure

high-quality ground truth. Custom scrapers and APIs

were used to retrieve real-world data while ensuring

compliance with platform policies. Table 1 shows the

Summary of Collected Dataset across Platforms and

Languages.

Table 1: Summary of Collected Dataset Across Platforms and Languages.

Platform

Language

Total Samples

Hate Speech (%)

Non-Hate (%)

Twitter

English

30,000

40%

60%

Facebook

Arabic

20,000

35%

65%

English

25,000

50%

YouTube

Code-Mixed (Hi-En)

15,000

45%

55%

Twitter

Marathi

10,000

38%

62%

3.2 Preprocessing & Fine-Grained

Annotation

The preprocessing pipeline involved:

• Noise Removal: Eliminating URLs, emojis

(unless semantically relevant), and non-standard

characters.

• Language Detection: Handling multilingual

and code-mixed content using custom detectors.

• Balanced Labeling: Multi-label annotations

assigned across categories such as racial abuse,

misogyny, religious hatred, xenophobia.

A fine-grained annotation schema was designed

to distinguish overlapping hate speech categories,

addressing multi-label complexities.

3.3 Context-Aware Tokenization &

Embedding

Instead of default tokenizers, the system employs:

• Dynamic WordPiece Tokenization

customized for regional slangs and mixed-

language expressions.

• Contextual Embeddings fine-tuned for

handling sarcasm, implicit hate, and code-

switching patterns.

This design improves representation for nuanced and

informal language typically missed by standard

models.

3.4 BERT-Based Model with

Multilingual Adapters

The core classification model architecture includes:

• BERT Backbone: Pretrained transformer

adapted to multilingual content.

• Adapter Modules: Light-weight plug-ins

tailored to specific languages or dialects without

full retraining.

Adapters allow quick domain-specific specialization

without model bloating, maintaining efficient

deployment. Figure 1 gives the flowchart of full

operational pipeline.

Figure 1: Flowchart of Full Operational Pipeline.

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3.5 Hierarchical Multi-Label Hate

Speech Classification

A hierarchical classification mechanism was

designed:

• Level 1: Binary classification Hate vs. Non-

Hate.

• Level 2: Fine-grained multi-label classification

among hate subcategories.

This multi-layer approach enhances precision in

handling complex and intersecting hate categories.

3.6 Explainability Module: SHAP and

LIME Integration

To ensure transparency and interpretability:

• SHAP (SHapley Additive Explanations):

Provides per-token contribution scores toward

hate classification.

• LIME (Local Interpretable Model-Agnostic

Explanations): Generates simplified,

interpretable explanations at the sentence level.

Explainability dashboards were built for

moderator inspection, supporting human-in-the-loop

decision-making (table 2)

Table 2: Explainability Impact Feedback.

Evaluation Criteria

% Positive

Feedback

Understandable

Explanations

91%

Helpful in Decision-

Making

88%

Improved Trust in System

85%

Visual Aids Usefulness

89%

3.7 Real-Time Inference and Filtering

(ONNX Optimized)

For real-time moderation, the system is:

• ONNX-optimized: Enabling deployment on

resource-constrained edge devices and high-

performance cloud servers.

• Lightweight Variants: Use of DistilBERT

for low-latency environments while

maintaining competitive accuracy. Latency

and Deployment Benchmark is given in

table 3.

Table 3: Latency and Deployment Benchmark.

Model

Variant

Deployment

Type

Avg.

Latenc

y (ms)

Max.

Throughput

(req/sec)

DistilBERT

Edge (Jetson

Nano)

82 ms

DistilBERT

Cloud (AWS

EC2)

47 ms

Full BERT

Cloud (GPU)

103 ms

3.8 Lexicon Expansion Module

(Weekly Hate Term Mining)

A dynamic lexicon mining module is deployed to:

• Analyze trending terms.

• Extract emerging slurs or offensive expressions

weekly.

• Retrain and fine-tune the model with minimal

disruption.

Automated updates ensure relevance and adaptability

in fast-changing social media environments.

3.9 Continual Learning-Based Model

Update

The continual learning pipeline incrementally

updates model parameters using:

• Mini-batch updates from validated new data.

• Pseudo-labeling for newly detected patterns.

• Elastic Weight Consolidation (EWC) to avoid

catastrophic forgetting.

This ensures the model maintains its old knowledge

while adapting to new linguistic trends.

3.10 Moderator Feedback Loop &

Annotation Refinement

A closed feedback loop was integrated:

• Moderators provide feedback on false

positives/negatives.

• Annotations are refined in periodic cycles.

• Model is retrained on curated corrections to

continuously improve sensitivity and fairness.

3.11 Performance Monitoring and

Evaluation

The framework’s evaluation covers:

• Precision, Recall, Accuracy, and F1-score

metrics across all datasets and subcategories.

• Bias testing using demographic subgroup

evaluations.

• Explainability assessment through moderator

surveys.

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4 RESULTS AND DISCUSSION

The proposed BERT-based NLP framework was

evaluated on a multi-platform, multilingual dataset

comprising over 100,000 annotated posts across

English, Arabic, Marathi, and Hindi-English code-

mixed content. The model demonstrated strong

performance across all key metrics. It achieved an

average F1-score of 92.6% in detecting hate speech,

outperforming baseline models such as HateBERT

(86.3%), XLM-R (88.1%), and a fine-tuned

RoBERTa (89.4%). The integration of a hierarchical

multi-label classifier enabled the system to accurately

distinguish between overlapping categories of hate

speech, such as misogyny and racial abuse, where

other models often misclassified or merged the labels.

Notably, the inclusion of user-history-aware

contextual embeddings improved the model’s

precision in detecting implicit or sarcastic hate

content by 9%, showcasing its ability to interpret

nuanced and coded language more effectively than

traditional classifiers. Table 4 and figure 2 shows the

model performance comparison.

Table 4: Model Performance Comparison.

Model

Accurac

y (%)

Precisio

n (%)

Recall

(%)

F1-

Score

(%)

Proposed

Model

94.2

93.1

92.2

92.6

HateBERT

88.4

87.2

85.1

86.3

XLM-R

89.8

89.0

87.5

88.1

RoBERTa +

fastText

90.5

89.3

89.6

89.4

Latency tests were conducted in both cloud and

edge environments, with the DistilBERT variant

achieving an average response time of 82 ms,

validating the system's capacity for real-time

filtering. This low-latency performance makes it

suitable for live comment moderation and real-time

alert systems. The continual learning mechanism also

proved to be effective, enabling the model to

incorporate emerging hate terms with minimal

retraining. When exposed to unseen slurs introduced

post-initial training, the model, with continual

updates, retained 87.2% accuracy, compared to

72.5% without the continual learning component.

Furthermore, the explainability features

implemented using SHAP and LIME provided

interpretable visual and textual outputs. In moderator

usability tests, 91% of participants reported that

explanations helped them understand why content

was flagged, supporting the model’s practical

deployment for content moderation teams. The

lexicon expansion module, which updated weekly,

showed a 13% boost in recall over a one-month trial

as new slang and regional hate terms were

dynamically integrated.

Figure 2: Model Performance Comparison.

Despite the high performance, some limitations

were observed. The model's accuracy dipped slightly

in highly noisy and informal datasets containing

emojis, mixed languages, and abbreviated slang not

seen in training. Nevertheless, improvements in both

fine-tuning and real-time feedback integration can

help bridge this gap. To sum up, the results confirm

that the proposed model is effective, flexible and

interpretable and we see it as a promising scalable

approach for hate speech on global social media.

Figure 3 shows the Word importance based on SHAP.

Figure 3: Word Importance Based on Shap.

5 CONCLUSIONS

This research introduces a fully scalable BERT-based

NLP framework for automated hate speech detection

and filtering in online social platforms. By

responding to the shortcomings in current models:

multilingual in performance, lack of context

rendition, lack of real-time and explainability, the

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proposed system provides a new approach, which is

accurate, flexible and transparent. By combining

making continual learning, the multilanguage adapter

modules with the hierarchical multi-label

classification the model is able to generalize well on

diverse and emerging modes of hate speech

including subtle, implicit, context dependent hate

speech expressions.

The system’s low latency, which is implemented

based on more compact BERT versions and ONNX

optimization, makes the system feasible for real-time

deployment on cloud and edge. And there are

explainability-based tools powered by SHAP and

LIME which offer valuable explanations for model

decisions, enabling trust and interpretability for

moderators and users of the platform. Continuous

expansion of hate lexicon and retraining in real-time

help in making the model well-equipped to new and

emerging hate terms.

Through extensive empirical studies, we can

conclusively prove the efficiency of our framework

compared to traditional baselines of precision, recall,

F1-score, and fairness, with strong transferability

across different languages and platforms. This work

establishes a new state of the art for designing

intelligent, ethical, and high-performance models in

hate speech detection, thereby enabling safer and

more inclusive digital spaces. Possible future

improvements are interfacing with multimedia

content analysis and reinforcement learning to

include feedback of moderator.

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