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https://github.com/lasyakonduru/unraveling-viral-news-across-social-media

Decoding the Dynamics of Social Media Engagement and Article Popularity
https://github.com/lasyakonduru/unraveling-viral-news-across-social-media

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Decoding the Dynamics of Social Media Engagement and Article Popularity

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README 

        
# 📊 Unraveling Viral News: Decoding the Dynamics of Social Media Engagement and Article Popularity



Welcome to the repository for **Unraveling Viral News** — a data-driven exploration into the factors that make news articles go viral across social media platforms. This project was developed as part of my final School assignment at **Saint Louis University** under the guidance of **Professor Ravindranth Arunasalam**.



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## 🚀 Project Overview



With social media shaping the way we consume information, understanding **why some articles attract massive engagement while others do not** has become critical. This project aims to:

- Analyze **platform-specific trends** (Facebook, LinkedIn, GooglePlus).

- Explore the impact of **sentiment, topics, hashtags, mentions, and sources** on article engagement.

- Build **predictive models** to identify characteristics that make content viral.



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## 🛠️ Key Features



### 🔍 Data Analysis

- **Sentiment Analysis**: BERT-based sentiment scoring for titles and headlines.

- **Engagement Metrics**: Analysis of likes, shares, comments, hashtags, and mentions.

- **Platform Trends**: Comparison of engagement across Facebook, LinkedIn, and GooglePlus.



### 📚 Topic Modeling

- Leveraged **Latent Dirichlet Allocation (LDA)** and clustering techniques to identify trending topics.

- Explored **word frequency, bigrams, and trigrams** to understand headline patterns.



### 📈 Predictive Modeling

- Built **Random Forest** and **Polynomial Regression** models to predict engagement.

- Evaluated feature importance to identify the most impactful article characteristics.



### 📅 Time-Based Analysis

- Studied how headline characteristics and engagement trends evolved over time.

- Focused on key events like the 2016 elections.



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## 💡 Research Questions



1. **Which news article characteristics make them viral across different social media?**

2. **Is there any topic that makes some news more viral than others across platforms and changes over time?**

3. **How does user sentiment in using likes, shares, and comments predict the popularity of news articles?**



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## 📊 Key Insights



- Articles with **positive or neutral sentiment** tend to perform better across platforms.

- Certain topics (e.g., "Obama," "Economy") consistently attract higher engagement.

- **Hashtags and mentions** significantly influence virality, with trending keywords amplifying reach.

- **Headline patterns**, such as the use of numbers ("Top 10") and emotional words, play a critical role in driving engagement.



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## 📁 Repository Structure



- **`/data`**: Contains the dataset used for analysis.

- **`/notebooks`**: Jupyter notebooks for data exploration, analysis, and modeling.

- **`/models`**: Trained models and scripts for predictive analysis.

- **`/visualizations`**: Charts and plots generated during the project.



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## 🛠️ Tools & Technologies

- **Python**: Core programming language.

- **Pandas, NumPy**: Data manipulation and analysis.

- **Matplotlib, Seaborn**: Data visualization.

- **NLTK, Scikit-learn**: NLP and machine learning.

- **BERT**: Sentiment analysis.



## 🌟 Acknowledgments

This project was guided and supervised by Professor Ravindranth Arunasalam, whose expertise and insights were invaluable throughout this journey.



## 📬 Connect with Me

- **LinkedIn**: https://www.linkedin.com/in/lasya-priya-k/

- **Email**: [email protected]

- **GitHub**: lasyakonduru



## 🤝 Contributions

Contributions are welcome! If you’d like to enhance this project, feel free to fork the repository, submit issues, or create pull requests.



## 📜 License

This `README.md` file provides a comprehensive overview of my project, instructions for running it, and an invitation for collaboration.