|
--- |
|
title: Misinformation Truthteller |
|
emoji: 📊 |
|
colorFrom: indigo |
|
colorTo: indigo |
|
sdk: streamlit |
|
sdk_version: 1.41.1 |
|
app_file: app.py |
|
pinned: false |
|
license: mit |
|
short_description: It is to get the model to tell truth about real world news |
|
--- |
|
# Misinformation Detection Tool |
|
|
|
## Overview |
|
|
|
Misinformation has become a significant issue in today's digital age, influencing public opinion and spreading unreliable news. This project addresses the problem by building a robust **Misinformation Detecting Tool** using advanced **Hugging Face Transformers**. The system is capable of identifying whether a given news article or statement is genuine or fake. |
|
|
|
## Problem Statement |
|
|
|
The rapid spread of misinformation through online platforms and social media has created the need for reliable tools to combat fake news. Identifying fake news manually is time-consuming and prone to bias. This project automates the detection of fake news using natural language processing (NLP) techniques, ensuring scalability and accuracy. |
|
|
|
## Objective |
|
|
|
The objective of this project is to develop and deploy a machine learning model capable of analyzing textual data and accurately classifying it as either **real** or **fake** news. The solution is deployed using Hugging Face Transformers to make it accessible and scalable. |
|
|
|
## Features |
|
|
|
- **Deep Learning Model**: Built on Hugging Face Transformers for state-of-the-art text classification. |
|
- **Scalable Deployment**: Deployed on Hugging Face for seamless integration and access. |
|
- **Real-Time Prediction**: Provides instant results for news articles or headlines. |
|
|
|
## Methodology |
|
|
|
1. **Data Collection**: |
|
- Collected datasets from Kaggle and other reliable sources containing labeled news articles. |
|
2. **Data Preprocessing**: |
|
|
|
- Cleaned and tokenized text data. |
|
- Removed stop words, special characters, and performed lemmatization. |
|
|
|
3. **Model Selection**: |
|
|
|
- Used a pre-trained transformer model (e.g., BERT, RoBERTa) from Hugging Face. |
|
- Fine-tuned the model on the fake news dataset. |
|
|
|
4. **Training**: |
|
|
|
- Split the dataset into training and validation sets. |
|
- Used PyTorch backend for training with optimization techniques. |
|
|
|
5. **Evaluation**: |
|
|
|
- Measured performance using metrics like accuracy, precision, recall, and F1-score. |
|
- Validated the model with a test dataset to ensure generalizability. |
|
|
|
6. **Deployment**: |
|
- Deployed the model on Hugging Face for public access. |
|
- API created for real-time predictions. |
|
|
|
## Scope |
|
|
|
- **Immediate Use**: Detects fake news effectively from textual inputs such as headline or article links. |
|
- **Future Enhancements**: |
|
- Incorporating language detection and translation for multilingual support. |
|
- Extending the dataset to include more diverse topics and sources. |
|
- Integration with video and audio analysis for multimedia content. |
|
- Expanded database for fact-checking and knowledge graphs. |
|
|
|
## Installation and Usage |
|
|
|
### Local Setup |
|
|
|
1. Clone the repository: |
|
|
|
```bash |
|
git lfs install |
|
git clone https://huggingface.co/spaces/malavika4089/misinformation-truthteller/tree/main |
|
cd misinformation-truthteller |
|
``` |
|
|
|
2. Install dependencies: |
|
|
|
```bash |
|
pip install -r requirements.txt |
|
``` |
|
|
|
3. Run the script: |
|
```bash |
|
streamlit run app.py |
|
``` |
|
|
|
### Access Deployed Model |
|
|
|
The model is deployed on Hugging Face. You can access it [Live link](https://huggingface.co/spaces/malavika4089/misinformation-truthteller). |
|
|
|
## Dataset |
|
|
|
The dataset used for this project was sourced from: |
|
|
|
- [Kaggle Fake And Real News Dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) |
|
|
|
## Technologies Used |
|
|
|
- **Programming Language**: Python |
|
- **Libraries**: Hugging Face Transformers, PyTorch, Scikit-learn, NumPy, Pandas, streamlit |
|
- **Deployment**: Hugging Face Spaces, |
|
- **Tools**: Colab |
|
|
|
## License |
|
|
|
This project is licensed under the [MIT License](LICENSE). |
|
|
|
|
|
|
