Malayalam Fake News Detection System: NLP Practice for Low-Resource Languages

This article introduces an AI fake news detection system for Malayalam, integrating Transformer models and machine learning techniques to provide a complete solution from data preprocessing to real-time classification. The project aims to address the lack of NLP tools for low-resource languages (such as Malayalam), provide references for NLP applications in similar languages, and help narrow the digital divide.

The spread of fake news in the digital age has become a global issue, but existing detection technologies are mostly focused on high-resource languages like English. Malayalam, as the main language of Kerala, India (with 38 million speakers), faces challenges of data scarcity and insufficient tools in the NLP field. Its complex character combinations (ligatures, vowel diacritics) from the Brahmic script also increase the difficulty of text processing.

The project adopts a hybrid architecture of traditional machine learning and deep learning: traditional methods are stable when data is limited, while deep learning (e.g., Transformer) captures complex semantics. Domain adaptation is performed using multilingual pre-trained models (mBERT/XLM-R) to avoid the resource consumption of training large models from scratch.

The system includes four core modules: 1. Data preprocessing (text normalization, word segmentation, stopword removal, adapted to Malayalam characteristics); 2. Model training framework (supports multiple algorithms such as Naive Bayes, SVM, LSTM, BERT variants); 3. Dataset management (annotation, format conversion, splitting into training/validation/test sets); 4. Real-time classification system (receives text/URL input and outputs detection results).

The system can be applied in: 1. Social media content moderation (assisting manual marking of suspicious content); 2. Fact-checking for news agencies (quickly identifying reports that require in-depth investigation); 3. Public media literacy education (open-source project helps communities understand detection technology).

Current limitations include: potential bias in training data, vulnerability to adversarial content attacks, and lack of interpretability in decisions. Future directions: continuously optimize data quality, address adversarial content, improve model interpretability; also adapt to other low-resource languages and iterate improvements through open-source communities.

This project not only solves the practical problem of Malayalam fake news detection but also provides a reusable technical blueprint for low-resource language NLP. It helps narrow the digital divide, allowing more low-resource language users to benefit from AI technology, and serves as a valuable reference for technical developers in building NLP systems under resource-constrained scenarios.