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https://github.com/darsan-in/rumour-monger-spotter

Rumour Monger Spotter is a prototype developed during a national-level cyber hackathon to identify false information on Twitter. Using the Google Fact Check API and a Multinomial Naive Bayes classifier, the tool analyzes tweet content to assess the likelihood of misinformation. Despite a development window of less than 24 hours, the project won a t
https://github.com/darsan-in/rumour-monger-spotter

ai data-analysis fact-checking hackathon india naive-bayes national-competition natural-language-processing prototype real-time-analysis social-media text-classification tweet-content twitter

Last synced: 15 days ago
JSON representation

Host: GitHub
URL: https://github.com/darsan-in/rumour-monger-spotter
Owner: darsan-in
License: mit
Created: 2023-01-20T05:27:47.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2024-11-10T08:07:16.000Z (about 2 months ago)
Last Synced: 2024-12-06T09:58:43.491Z (20 days ago)
Topics: ai, data-analysis, fact-checking, hackathon, india, naive-bayes, national-competition, natural-language-processing, prototype, real-time-analysis, social-media, text-classification, tweet-content, twitter
Language: Python
Homepage:
Size: 24.5 MB
Stars: 3
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

# Rumour Monger Spotter - Detecting Misinformation on Twitter

Rumour Monger Spotter is a prototype developed during a national-level cyber hackathon to identify false information on Twitter. Using the Google Fact Check API and a Multinomial Naive Bayes classifier, the tool analyzes tweet content to assess the likelihood of misinformation. Despite a development window of less than 24 hours, the project won a trophy for its unique approach in a competition with 150+ teams.

### Supported Platforms

[![Python](https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white)]()

---

## Table of Contents 📝

- [Features and Benefits](#features-and-benefits-)
- [Use Cases](#use-cases-)
- [Friendly request to users](#-friendly-request-to-users)

- [Usage](#usage)
- [In-Action](#in-action-)

- [License](#license-%EF%B8%8F)
- [Contributing to Our Project](#contributing-to-our-project-)

- [Contact Information](#contact-information)

## Features and Benefits ✨

- **Twitter Content Analysis**: Extracts and processes tweet content to identify potential misinformation.
- **Google Fact Check Integration**: Leverages the Google Fact Check API to verify the truthfulness of information in tweets.
- **Machine Learning Classification**: Uses a Multinomial Naive Bayes classifier to predict the likelihood of a tweet being false.
- **Contextual Tag Extraction**: Analyzes tweet tags to understand the context and content more accurately.
- **False vs. True Ratio Calculation**: Calculates the ratio of true to false information within a tweet to provide a likelihood score.
- **Rapid Prototyping**: Developed in under 24 hours, demonstrating feasibility under extreme time constraints.

## Use Cases ✅

- **Misinformation Detection**: Identifying false information in tweets for fact-checkers and social media platforms.
- **Content Moderation**: Assisting platforms in moderating and flagging potential misinformation.
- **Academic Research**: Providing a foundation for further research into misinformation detection algorithms.
- **Hackathon Prototyping**: Serving as a proof of concept in rapid development scenarios.
- **AI and NLP Education**: Demonstrating the application of AI and natural language processing techniques in real-world scenarios.

---

### 🙏🏻 Friendly Request to Users

Every star on this repository is a sign of encouragement, a vote of confidence, and a reminder that our work is making a difference. If this project has brought value to you, even in the smallest way, **please consider showing your support by giving it a star.** ⭐

_"Star" button located at the top-right of the page, near the repository name._

Your star isn’t just a digital icon—it’s a beacon that tells us we're on the right path, that our efforts are appreciated, and that this work matters. It fuels our passion and drives us to keep improving, building, and sharing.

If you believe in what we’re doing, **please share this project with others who might find it helpful.** Together, we can create something truly meaningful.

Thank you for being part of this journey. Your support means the world to us. 🌍💖

---

## Usage

- **Step 1:** Install Python, it's not already installed.

- **Step 2:** Clone this repository and open it in VS Code or any IDE.

```bash
git clone https://github.com/darsan-in/Rumour-Monger-Spotter.git
```

- **Step 3:** Open terminal and run batch script to resolve requirements.

```bash
requirements
```

- **Step 4:** Before running program, you have to get your credential for Google Fact API and Twitter API, then add them into `prodx/lib/credential.py`.

- **Step 5:** Now you can run the program.

```bash
run
```

- **Step 6:** Click to open the link that showing in the terminal window.

- **Step 7:** Paste any tweet link in application text box input.

- **Step 8:** Results would be shown in terminal window.

## In-Action 🤺

![home page of trackx](image.png)

## License ©️

This project is licensed under the [MIT](LICENSE).

## Contributing to Our Project 🤝

We’re always open to contributions and fixing issues—your help makes this project better for everyone.

If you encounter any errors or issues, please don’t hesitate to [raise an issue](../../issues/new). This ensures we can address problems quickly and improve the project.

For those who want to contribute, we kindly ask you to review our [Contribution Guidelines](CONTRIBUTING) before getting started. This helps ensure that all contributions align with the project's direction and comply with our existing [license](LICENSE).

We deeply appreciate everyone who contributes or raises issues—your efforts are crucial to building a stronger community. Together, we can create something truly impactful.

Thank you for being part of this journey!

## Contact Information

For any questions, please reach out via [email protected] or [LinkedIn](https://www.linkedin.com/in/darsan-in/).

---