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base_model: roberta-base
library_name: peft
pipeline_tag: text-classification
language:
- en
tags:
- misinformation-detection
- social-media
- fakett
- peft
- lora
- transformers
- base_model:adapter:roberta-base
---
# RoBERTa — Text-Only Misinformation Detection on FakeTT
**Authors:** Andrei-Gabriel Radu, Ciprian-Octavian Truică, Elena-Simona Apostol
**National University of Science and Technology POLITEHNICA Bucharest**
LoRA adapter fine-tuned from `roberta-base` for binary **text-only misinformation classification** on the FakeTT social-media video dataset.
This model accompanies the bachelor thesis *Misinformation Detection in Social Media Videos*.
## Results
| Dataset | Modality | Macro-F1 |
|---|---|---:|
| FakeTT | Text-only | 0.8443 |
## Model
- **Base model:** `roberta-base`
- **Task:** Binary misinformation classification
- **Modality:** Text-only
- **Fine-tuning:** LoRA / PEFT
- **Dataset:** FakeTT
- **Number of classes:** 2
- **Primary metric:** Macro-F1
## Training
- **LoRA rank (`r`):** 16
- **LoRA alpha:** 16
- **LoRA dropout:** 0.05
- **Target modules:** `value`, `query`, `key`, `dense`
- **Bias:** none
## Usage
```python
import torch
from peft import PeftModel
from transformers import AutoTokenizer, AutoModelForSequenceClassification
repo_id = "DS4AI-UPB/roberta-misinfo-lora"
base_model_id = "roberta-base"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
base_model = AutoModelForSequenceClassification.from_pretrained(base_model_id, num_labels=2)
model = PeftModel.from_pretrained(base_model, repo_id).eval()
text = "Example social media video description."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
logits = model(**inputs).logits
print(logits.argmax(dim=-1).item())
```
> Use the class-to-label mapping from the original FakeTT training pipeline.
## Intended Use
Research and benchmarking of English-language text-only misinformation detection for social-media video content.
## Limitations
This is a classification model, not a factual verification system. It cannot inspect the associated video and can degrade under domain shift.
## Citation
```bibtex
@thesis{radu2026misinformation,
author = {Radu, Andrei-Gabriel and Truică, Ciprian-Octavian and Apostol, Elena-Simona},
title = {Misinformation Detection in Social Media Videos},
school = {National University of Science and Technology POLITEHNICA Bucharest},
year = {2026}
}
```
|