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.
- 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.
- 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.
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Step 1: Install Python, it's not already installed.
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Step 2: Clone this repository and open it in VS Code or any IDE.
git clone https://github.com/darsan-in/Rumour-Monger-Spotter.git- Step 3: Open terminal and run batch script to resolve requirements.
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.
run-
Step 6: Click to open the link that showing in the terminal window.
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Step 7: Paste any tweet link in application text box input.
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Step 8: Results would be shown in terminal window.
This project is licensed under the MIT.
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. 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 before getting started. This helps ensure that all contributions align with the project's direction and comply with our existing 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!
For any questions, please reach out via hello@darsan.in or LinkedIn.
- fact-checking
- naive-bayes
- hackathon
- real-time-analysis
- natural-language-processing
- social-media
- AI
- text-classification
- prototype
- india
- national-competition
- data-analysis
- tweet-content