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| title: News Veracity Checker | |
| emoji: 📰 | |
| colorFrom: indigo | |
| colorTo: green | |
| sdk: streamlit | |
| sdk_version: 1.45.1 | |
| app_file: app.py | |
| pinned: false | |
| # 📰 News Veracity Checker | |
| With the explosive growth of social media, the spread of **fake news** has become a global crisis. According to the MIT Media Lab, **false information spreads six times faster** than true news on platforms like Twitter. Studies also show that nearly **64% of adults have been exposed to fake news online**, often without even realizing it. | |
| That’s why I built this project: an **News Veracity Checker Using AI**. This tool classifies whether a news article or headline is **true or false** and provides a **fact-based explanation using an open-source language model**. | |
| --- | |
| ## 💡 Features | |
| - ✅ **Veracity Score** – Classifies news as true or false using a BERT-based fake news detector | |
| - 📃 **Article Summarizer** – Provides a concise summary of the content using Facebook’s BART model | |
| - 🤖 **AI Explanation** – Uses Google’s Gemma-2B-Instruct LLM to generate fact-based rationales and corrections | |
| - ☁️ **Word Cloud Visualization** – Visual insight into the most frequent keywords | |
| - 🧠 **All models are run locally using Hugging Face Transformers** | |
| --- | |
| ## 🛠️ Tech Stack | |
| - **Streamlit** – For interactive front-end interface | |
| - **Hugging Face Transformers** – For veracity detection, summarization, and explanation generation | |
| - **PyTorch** | |
| - **Matplotlib & WordCloud** – For visualization | |
| - **Models Used:** | |
| - `jy46604790/Fake-News-Bert-Detect` – for veracity classification | |
| - `facebook/bart-large-cnn` – for summarizing long news texts | |
| - `google/gemma-2b-it` – for AI-generated factual explanation |