Implement Zero-Shot Cross-Lingual Misinformation Detection with Code-Switching Support for Indian Languages

[BhakktiGautam]

##Description
Transform ShadowTrace's ContentAnalyzer to detect misinformation across India's linguistic diversity without requiring labeled data for each language. Support code-switching (Hinglish, Tanglish, etc.) and handle low-resource languages with minimal supervision.

##Core Challenges
1.Language Diversity: 22+ official languages + 100+ regional dialects
2.Code-Switching: Hinglish, Tanglish, Manglish, etc. (mix of English + local language)
3.Low-Resource: Many languages have limited NLP datasets
4.Script Variations: Devanagari, Roman, Tamil, etc.
5.Cultural Nuance: Misinformation signals vary by cultural context
6.Real-Time: <100ms detection latency required

## Technical Implementation

backend/agents/multilingual_analyzer.py

class MultilingualMisinformationDetector:
"""
Zero-shot cross-lingual misinformation detection
"""
def init(self):
# 1. Language Identification Layer
self.language_detector = FastTextLanguageDetection(
supported_languages=['hi', 'bn', 'te', 'ta', 'ml', 'kn', 'ur', 'pa',
'or', 'gu', 'mr', 'ne', 'si', 'en', 'hybrid']
)

    # 2. Multilingual Embedding Model
    self.embedding_model = XLMRobertaModel.from_pretrained(
        'xlm-roberta-large',
        cache_dir='./models/xlm-roberta'
    )
    
    # 3. Code-Switching Tokenizer
    self.code_switching_tokenizer = CodeSwitchingTokenizer(
        languages=['hi', 'en', 'bn', 'te'],
        script_handling='roman_to_unicode'
    )
    
    # 4. Zero-Shot Classifier
    self.classifier = ZeroShotClassifier(
        pretrained='xlm-roberta-large',
        hypothesis_template="This text contains {} misinformation"
    )
    
    # 5. Cultural Context Adapter
    self.cultural_adapter = CulturalContextAdapter(
        reference_corpora=self.load_regional_corpora(),
        region_specific_signals=self.get_region_signals()
    )
    
async def analyze(self, text: str, region: str = None):
    """
    Analyze text for misinformation across languages
    """
    # Step 1: Detect language(s)
    languages = self.language_detector.detect(text)
    
    # Step 2: Handle code-switching
    if len(languages) > 1 or 'hybrid' in languages:
        normalized_text = self.code_switching_tokenizer.normalize(text)
        language_segments = self.code_switching_tokenizer.segment(text)
    else:
        normalized_text = text
        language_segments = [(text, languages[0])]
    
    # Step 3: Generate multilingual embeddings
    embeddings = []
    for segment, lang in language_segments:
        embedding = self.embedding_model.encode(segment, lang=lang)
        embeddings.append(embedding)
    
    # Step 4: Zero-shot classification
    classifications = []
    for segment, lang in language_segments:
        # Detect misinformation across categories
        categories = ['political', 'health', 'scientific', 'religious', 'communal']
        scores = self.classifier.classify(segment, categories)
        classifications.append({
            'segment': segment,
            'language': lang,
            'scores': scores
        })
    
    # Step 5: Apply cultural context
    region = region or self.detect_region(text, languages)
    cultural_weights = self.cultural_adapter.get_weights(region)
    
    # Step 6: Aggregate with cultural weighting
    final_score = self.aggregate_scores(
        classifications, 
        cultural_weights,
        self.get_language_confidence(languages)
    )
    
    # Step 7: Generate language-specific signals
    signals = self.extract_multilingual_signals(
        text=text,
        languages=languages,
        classifications=classifications,
        region=region
    )
    
    return {
        'is_misinformation': final_score > 0.7,
        'confidence': final_score,
        'languages_detected': languages,
        'region': region,
        'code_switching_detected': len(languages) > 1,
        'signals': signals,
        'segment_analysis': classifications
    }

def extract_multilingual_signals(self, text, languages, classifications, region):
    """
    Extract language-specific misinformation signals
    """
    signals = {
        'urgency_language': self.detect_urgency(text, languages),
        'authority_claims': self.detect_authority_claims(text, region),
        'emotional_appeal': self.detect_emotion(text, languages),
        'conspiracy_markers': self.detect_conspiracy_terms(text, languages),
        'cultural_triggers': self.detect_cultural_triggers(text, region)
    }
    
    # Language-specific signals
    for lang in languages:
        if lang == 'hi':
            signals.update(self.detect_hindi_misinformation_signals(text))
        elif lang == 'bn':
            signals.update(self.detect_bengali_misinformation_signals(text))
        # ... other languages
    
    return signals

##Supporting Components
python

backend/utils/code_switching.py

class CodeSwitchingTokenizer:
"""
Handle Hinglish/Tanglish/Manglish code-switching
"""
def init(self):
self.roman_to_unicode = RomanToUnicodeConverter()
self.unicode_to_roman = UnicodeToRomanConverter()
self.segmenter = LanguageSegmenter()

def normalize(self, text: str) -> str:
    """
    Normalize code-switched text to standard form
    """
    # Handle common Hinglish patterns
    text = self.normalize_hinglish(text)
    text = self.normalize_tanglish(text)
    text = self.normalize_manglish(text)
    return text

def segment(self, text: str) -> List[Tuple[str, str]]:
    """
    Segment text into language segments
    """
    return self.segmenter.segment_by_language(text)

Training Strategy
python

backend/training/multilingual_training.py

class MultilingualTrainingPipeline:
"""
Train multilingual misinformation detection models
"""
def init(self):
self.language_adapters = {}

def prepare_zero_shot_data(self):
    """
    Prepare multilingual data for zero-shot learning
    """
    # 1. Translate English misinformation to all languages
    # 2. Create synthetic code-switched data
    # 3. Use semantic similarity for cross-lingual transfer
    pass

def continual_learning(self, new_data):
    """
    Continual learning for emerging languages/signals
    """
    # 1. Detect new language patterns
    # 2. Update model with minimal forgetting
    # 3. Validate on existing languages
    pass

[BhakktiGautam]

@uttampreet-dev Please assign me this issue under GSSOC'26.