erikbranmarino/Mistral-PRCT

🔍 Mistral-PRCT

A LoRA fine-tuned Mistral-7B model for detecting Population Replacement Conspiracy Theory (PRCT) content across (at least) Portuguese Telegram and Italian news headlines.

 

Overview

Mistral-PRCT is a LoRA adapter fine-tuned on Portuguese Telegram messages for detecting Population Replacement Conspiracy Theories. The model demonstrates strong cross-domain generalization, achieving competitive performance on Italian news headlines despite being trained exclusively on informal social media discourse.

Key Metrics

Model Description

Mistral-PRCT is a LoRA (Low-Rank Adaptation) fine-tuned version of Mistral-7B-Instruct-v0.3, specifically adapted for detecting Population Replacement Conspiracy Theory (PRCT) content. The model was trained on Portuguese Telegram messages and demonstrates robust cross-domain transfer to Italian news headlines.

What are PRCTs?

Population Replacement Conspiracy Theories are false narratives claiming deliberate orchestration of demographic substitution through immigration. Main variants include:

• —The Great Replacement Theory
• —White Genocide
• —Kalergi Plan
• —Eurabia

These narratives are linked to extremist violence (Christchurch 2019, Utøya 2011) and pose serious threats to democratic discourse.

Model Configuration

Architecture

• —Base Model: Mistral-7B-Instruct-v0.3 (7B parameters)
• —Adapter Type: LoRA (Low-Rank Adaptation)
• —LoRA Rank: 16
• —LoRA Alpha: 32
• —Target Modules: qproj, vproj, kproj, oproj

Label Mapping

• —0: Non-PRCT content
• —1: PRCT content (supports/mentions replacement narratives)

Input Requirements

• —Maximum sequence length: 2048 tokens
• —Input type: Text (Portuguese, Italian, Spanish)
• —Preprocessing: Standard Mistral tokenization

Intended Uses & Limitations

✅ Intended Uses

• —AI-assisted content moderation (with human oversight)
• —Research on conspiracy theory propagation
• —Cross-domain and multilingual PRCT detection
• —Analysis of informal social media discourse

⚠️ Limitations

• —Training bias: Optimized for Portuguese Telegram messages
• —Cross-domain performance: 6.6pp F1-macro drop on formal news (expected)
• —Language coverage: Best on Portuguese, good on Italian, untested on other Romance languages
• —Inference cost: ~4.6s per sample (slower than zero-shot but higher accuracy)

Important: Should be used as part of a broader content moderation strategy, not as sole decision-maker.

Training Data

• —Primary training: Portuguese Telegram messages (n=919)
• —Domain: Informal social media discourse, conspiracy-oriented channels
• —PRCT prevalence: 15.7%
• —Annotation: Expert annotators (Krippendorff's α=0.58)
• —Time period: 2020-2024

Training Procedure

Hyperparameters

• —Learning rate: 2e-5
• —Batch size: 4 (with gradient accumulation)
• —Training steps: 600
• —Optimizer: AdamW 8-bit
• —LoRA dropout: 0.05
• —Weight decay: 0.01

Hardware

• —GPU: NVIDIA A100 40GB
• —Training time: ~2 hours
• —Framework: PyTorch + PEFT

Results

In-Domain Performance (Telegram PT)

Cross-Domain Performance (News ITA)

Key Finding: Training on informal Portuguese Telegram enhances detection of implicit PRCT framing in formal Italian news, demonstrating effective cross-domain transfer from social media to journalistic discourse.

Usage

Installation

### Basic Usage

from peft import PeftModel import torchLoad base model and tokenizer basemodelname = "mistralai/Mistral-7B-Instruct-v0.3" model = AutoModelForCausalLM.frompretrained( basemodelname, torchdtype=torch.float16, devicemap="auto" ) tokenizer = AutoTokenizer.frompretrained(basemodelname)Load LoRA adapter model = PeftModel.frompretrained(model, "erikbranmarino/Mistral-PRCT")Prepare prompt text = "Your Portuguese or Italian text here" prompt = f"""Classify if the following text contains Population Replacement Conspiracy Theory (PRCT) content.Text: {text}Classification (YES/NO):"""Generate prediction inputs = tokenizer(prompt, returntensors="pt").to(model.device) outputs = model.generate( **inputs, maxnewtokens=10, temperature=0.0, dosample=False ) prediction = tokenizer.decode(outputs[0], skipspecial_tokens=True)print(prediction)

Batch Processing Example

"""Classify multiple texts efficiently"""
predictions = []for i in range(0, len(texts), batch_size):
    batch = texts[i:i+batch_size]
    prompts = [f"Classify PRCT: {text}" for text in batch]    inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=10, temperature=0.0)    for output in outputs:
        pred = tokenizer.decode(output, skip_special_tokens=True)
        predictions.append(pred)return predictions

## Bias and Ethical Considerations

### Known Biases
- **Platform bias**: Optimized for Telegram-style informal discourse
- **Language bias**: Primarily Portuguese, with cross-lingual transfer to Italian
- **Temporal bias**: Training data from 2020-2024 may not capture evolving narratives

### Ethical Use
- ⚠️ **Not for automated censorship**: Requires human review
- ✅ **Research purposes**: Understanding conspiracy theory propagation
- ✅ **Content flagging**: Assisting moderators, not replacing them
- ❌ **Surveillance**: Not intended for monitoring individuals

We advocate for freedom of speech and constitutional rights. This tool should support informed moderation, not suppress legitimate discourse.

## Citation (to appear)

title={Population Replacement Conspiracy Theories Detection on Telegram and News Headlines: benchmarking LLMs and BERT models in Portuguese and Italian}, author={Marino, Erik Bran and Vieira, Renata}, booktitle={Proceedings of PROPOR 2026}, year={2026} }

Model Card Authors

Erik Bran Marino (Universidade de Évora, HYBRIDS Project)

Contact

• —Email: erik.marino@uevora.pt
• —Project: MSCA HYBRIDS (Grant Agreement No. 101073351)
• —Institution: Universidade de Évora, Portugal

License

MIT License - Free for research and educational purposes.

Developed as part of the HYBRIDS Marie Skłodowska-Curie Actions project