Multimodal Fake News Detection on Twitter involves the use of advanced algorithms and machine learning techniques to identify false information by analyzing multiple types of data, including text, images, and user interactions. By leveraging the diverse features of tweets, such as linguistic patterns, visual content, and network behavior, this approach aims to improve the accuracy and reliability of detecting misleading or deceptive posts on the platform.
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Multimodal Fake News Detection on Twitter involves utilizing various models to analyze different aspects of tweet posts, such as text, images, and metadata, to determine their authenticity. By evaluating the performance of multiple models individually, the most effective ones were selected and integrated into a unified system. This comprehensive approach enhances the accuracy of detecting fake news on Twitter by leveraging the strengths of each model to assess the legitimacy of tweet content.
- Clone the repo
git clone https://github.com/priyasu-cx/Multimodal-FND-on-Twitter.git
- Install pip packages
pip install -r requirements.txt
- Start streamlit app from Prototype folder
streamlit run app.py
Distributed under the MIT License. See LICENSE.txt for more information.
Priyasu Guin - @priyasu_cx - [email protected]
Project Link: https://github.com/priyasu-cx/Multimodal-FND-on-Twitter