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

License: MIT

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

Dataset	F1-Macro	F1-Binary	Accuracy
Telegram PT (in-domain)	0.819	0.700	0.896
News ITA (cross-domain)	0.753	0.688	0.771

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: q_proj, v_proj, k_proj, o_proj

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)

Metric	Score
Accuracy	0.896
Precision (Macro)	0.797
Recall (Macro)	0.848
F1-Macro	0.819
F1-Binary	0.700
Inference Time	4.62s/sample

Cross-Domain Performance (News ITA)

Metric	Score
Accuracy	0.771
Precision (Macro)	0.748
Recall (Macro)	0.786
F1-Macro	0.753
F1-Binary	0.688
Inference Time	4.50s/sample

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

bashpip

### Basic Usage
```pythonfrom transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torchLoad base model and tokenizer
base_model_name = "mistralai/Mistral-7B-Instruct-v0.3"
model = AutoModelForCausalLM.from_pretrained(
base_model_name,
torch_dtype=torch.float16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)Load LoRA adapter
model = PeftModel.from_pretrained(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, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=10,
temperature=0.0,
do_sample=False
)
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)print(prediction)

### Batch Processing Example
```pythondef classify_batch(texts, model, tokenizer, batch_size=8):
"""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)
```bibtex@inproceedings{marino2025prct,
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.

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