timesformer-misinfo-lora

TimeSformer — Video-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 facebook/timesformer-base-finetuned-k600 for binary video-only misinformation classification on FakeTT.

This model accompanies the bachelor thesis Misinformation Detection in Social Media Videos.

Results

Model

• Base model: facebook/timesformer-base-finetuned-k600
• Task: Binary misinformation classification
• Modality: Video-only
• Fine-tuning: LoRA / PEFT
• Dataset: FakeTT
• Number of classes: 2
• Primary metric: Macro-F1

Training

• LoRA rank (r): 8
• LoRA alpha: 32
• LoRA dropout: 0.05
• Target modules: key, value, query, dense
• Bias: none

Usage

from peft import PeftModel
from transformers import AutoImageProcessor, AutoModelForVideoClassification

repo_id = "DS4AI-UPB/timesformer-misinfo-lora"
base_model_id = "facebook/timesformer-base-finetuned-k600"

processor = AutoImageProcessor.from_pretrained(repo_id)
base_model = AutoModelForVideoClassification.from_pretrained(
    base_model_id, num_labels=2, ignore_mismatched_sizes=True
)
model = PeftModel.from_pretrained(base_model, repo_id).eval()

Use the same frame sampling and preprocessing procedure as during training before passing pixel_values to the model.

Intended Use

Research and benchmarking of video-only misinformation detection on short social-media videos.

Limitations

The model does not use titles, descriptions or other textual metadata. It can miss linguistic misinformation cues and can degrade under domain shift or a different frame-sampling strategy.

Citation

@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}
}