---
title: 'BanglaFake Dataset: Bangla Misinformation & Deepfake'
url: https://www.emergentmind.com/topics/banglafake-dataset
type: topic
---

# BanglaFake Dataset: Bangla Misinformation & Deepfake

BanglaFake Dataset

The BanglaFake dataset refers to a family of large-scale, curated corpora developed for the detection of misinformation, manipulation, and audio deepfakes in Bangla (Bengali) language contexts. The designation “BanglaFake” appears both as a generic term for early benchmark text datasets on Bangla fake news and as the explicit name of a released deepfake audio corpus. These datasets provide comprehensive, multi-modal resources with well-documented annotation protocols and detailed metadata, serving as the primary foundation for both classical and neural misinformation detection in low-resource Bangla.

## 1. Text-Based BanglaFake/BanFakeNews Datasets

### 1.1. Origins and Construction

The canonical BanglaFake text dataset originated with the BanFakeNews project, which compiled approximately 50,000 news items from 22 major Bangla news portals and several fact-checking platforms between 2018 and 2020 [2004.08789]. Authentic samples (≈48,700) were crawled from mainstream portals, while fake news instances (≈1,300)—including misleading/contextual misinformation, clickbait, and satire/parody—were assembled from jaachai.com, bdfactcheck.com, and Bangla satire sites. Metadata collection encompasses domain URL, publication time, mapped article categories (12 high-level unifications), headline–article relatedness, and attribution source (person/organization). Annotation involved undergraduate computer science students, with rigorous validation for fake-news items against fact-checking sites and duplication removal. Labels comprise a binary real/fake distinction, with internal subtypes: False Context, Clickbait, and Satire/Parody.

Following research reused and expanded this core resource. Notably, the BanFakeNews-2.0 dataset increased the size to 60,000 news items (47,000 authentic and 13,000 fake) across 13 news categories, with a more balanced distribution of fake-news items per topic and a thoroughly validated independent test set [2501.09604].

### 1.2. Dataset Structure and Statistics

The principal text datasets demonstrate:

- Total size: N = 48,678 real + 1,299 fake = 49,977 [2004.08789]; BanFakeNews-2.0: 47,000 real + 13,000 fake [2501.09604].
- Train/test/validation splits: canonical 70%/30% with a further 10% test set for early stopping; BanFakeNews-2.0 recommends 70%/15%/15%.
- Class imbalance: authentic items dominate (typically 97% real in early dataset; BanFakeNews-2.0 reduces this to ≈1:3 fake:real).
- News categories: typically 12 or 13, including National, International, Sports, Politics, Editorial, Miscellaneous, Education, Technology, Crime, Finance, Entertainment, Medical, and Religious.
- Per-article means: authentic $\mu_C = 1479.14$ characters, $\mu_W = 271.16$ words, $\mu_S = 21.15$ sentences; fake $\mu_C = 1428.19$, $\mu_W = 276.36$, $\mu_S = 23.62$.
- Subset with explicit metadata: ≈8,500 items include full source and headline–body relation fields.
- Distribution across categories is skewed, with real items concentrated in National and International, and fake items mostly in Miscellaneous and specific topical areas [2004.08789].

## 2. Preprocessing and Feature Engineering

Text preprocessing pipelines incorporate Unicode normalization, lowercasing, Bangla stopword removal (Stopwords-ISO), and optional punctuation/numeric normalization. Tokenization is applied at both word and character granularity.

Feature sets evaluated for classical and neural models include:

- Lexical: Word n-grams ($n=1,2,3$), character n-grams ($n=3,4,5$), TF–IDF weighting
- Syntactic: POS-tag frequency distribution (using 10 Bangla POS classes)
- Semantic: Article-level mean and standard deviation of pre-trained word embeddings (FastText 300-dim, Word2Vec 100-dim), with corpus coverage at 54–55%
- Metadata: Punctuation frequency, normalized Alexa-rank of domain, headline/body length
- Neural pipeline experiments employ end-to-end trainable embeddings with maximum lengths (e.g., 250 tokens or 512 subwords for Transformers) [2004.08789] [2404.01345] [2501.09604].

## 3. Benchmarking and Model Performance

Textual BanglaFake datasets have been evaluated with baselines, shallow ML, and advanced DL models:

- SVM (char-3-grams or all features): best fake-class F1 = 0.89–0.91
- CNN: F1 up to 0.59; Bi-LSTM + attention: F1 up to 0.53
- Multilingual BERT (fine-tuned): F1 = 0.68; monolingual BanglaBERT, SagorBERT, and mBERT variants: macro-F1 up to 0.87 on BanFakeNews-2.0
- Large language models: QLoRA-adapted BLOOM 560M achieves macro-F1 = 0.89 on BanFakeNews-2.0 [2501.09604]
- Human annotators achieve average fake-class F1 ≈ 0.65, consistently outperformed by state-of-the-art automated systems [2004.08789].

Text-based benchmarks employ precision, recall, F1, macro-F1, and area under the ROC curve as standard metrics, focusing particularly on fake-class (positive) detection.

## 4. Deepfake Audio: BanglaFake Speech Corpus

The BanglaFake name is explicitly attached to two major Bengali deepfake audio datasets: [2505.10885] and [2512.21702]. These corpora comprise:

- 12,260 real + 13,260 deepfake utterances (total 25,520 clips), each 6–7 seconds; mean duration ≈6.5s
- Real speech sourced from the SUST TTS corpus and Mozilla Common Voice (7 speakers, both genders, multi-dialect Bangla)
- Synthetic/deepfake speech generated using VITS-based TTS models (conditional VAE, flow-based latent prior, HiFi-GAN decoder), trained on SUST TTS
- All audio is WAV, 22,050 Hz, 16-bit PCM; transcripts in LJ-Speech CSV convention
- Slight class imbalance (fake:real ≈1.08), no speaker-level imbalance
- Accessible via HuggingFace and GitHub (permissive research licenses)

Audio evaluation integrates:

- Robust MOS (mean opinion score, human): naturalness = 3.40, intelligibility = 4.01 (five-point scale)
- Feature analysis via MFCC extraction and t-SNE visualization: real and fake utterances partially overlap in acoustic space, highlighting detection challenge [2505.10885]
- Benchmarking with CNN/RNN/transformer architectures (Wav2Vec2-XLSR-53, ResNet18, LCNN, ViT-B16, CNN-BiLSTM). Fine-tuned ResNet18 achieves accuracy = 79.17%, F1 = 79.12%, AUC = 84.37%, EER = 24.35%. Zero-shot transformer performance is much lower (accuracy <54%) [2512.21702].

## 5. Specialized Derivatives and Related Resources

Several variants for related NLP tasks and modalities have been derived from or linked to the core BanglaFake datasets:

- BanMANI corpus [2311.02570]: 800 social media items (Facebook posts/comments), labeled as “manipulated” vs. “non-manipulated” relative to 500 BanFakeNews reference articles, with gold spans identifying altered excerpts. Used for text-based manipulation and claim detection benchmarks; employs zero-shot/fine-tuned GPT models (fine-tuned GPT-3 ada achieves F1 = 65.77% on detection)
- Bengali Fake Review Detection (“BFRD”): food review–specific dataset from social media (1,339 fake, 7,710 authentic), with heavy code-mixed/romanized text and extensive text normalization. Weighted-ensemble transformer systems on augmented sets achieve F1 >0.98 [2308.01987]

## 6. Applications, Limitations, and Community Guidelines

The BanglaFake text and audio datasets underpin research on:

- Automated fake news filtering for Bangla web and social media applications
- Human-in-the-loop fact-checking and misinformation discovery workflows
- Benchmarking of language models, cross-lingual transfer, and data augmentation strategies
- Evaluation of deepfake–audio detection, adversarial speech synthesis, and anti-spoofing

Reported limitations include strong inherent class imbalance (especially in text, with early real/fake ratios near 39:1), source-domain and topical skew, limited annotation coverage for metadata fields, absence of user-demographic and propagation information, and loss of long-range context (some versions restrict context to headlines/body ≤30 tokens) [2004.08789] [2503.04781].

Researchers are advised to:

- Use stratified or oversampled splits to address class imbalance
- Disclose all metrics (including precision/recall by class and macro-F1)
- Share derived versions and harmonized splits for comparability
- Engage in domain expansion and annotation refinement (e.g., manipulating claim types), and adapt multi-modal extensions as needed [2501.09604] [2311.02570]

BanglaFake establishes the foundation for scalable, reproducible misinformation detection in Bangla language, supporting continual advances in text, audio, and multi-modal fake detection in low-resource settings.

Source: https://www.emergentmind.com/topics/banglafake-dataset