# Social Media Misinformation Dataset

This dataset contains simulated data related to social media interactions, comments, posts, and users. The data is generated using the Faker library in Python and is intended for use in research and analysis related to misinformation on social media platforms.

## Data Generation Process

The data was generated using the Faker library, which provides a convenient way to create realistic-looking fake data. Here's an overview of the data generation process:

1. Installation: Ensure you have the Faker library installed. You can install it using pip:

   pip install Faker

2. Data Generation Script: The data generation script (generate_data.py) uses Faker to create fake data for various attributes, including:

   - Comment count
   - Political affiliation
   - Comment ID
   - Age
   - Keywords
   - Interaction ID
   - Source credibility score
   - Like count
   - Gender
   - Language
   - Education level
   - Trust score
   - URL
   - Location
   - Name
   - Reply count
   - Interaction target ID
   - Likes count
   - User ID
   - Category
   - Source ID
   - Interaction timestamp
   - Source type
   - Share count
   - Reach
   - Content
   - Timestamp
   - Sentiment
   - Interaction content
   - Title
   - Post ID
   - Commentor user ID
   - Job title
   - Engagement
   - Interaction type
   - Tags

3. Data Format: The generated data is stored in a Pandas DataFrame and saved to an Excel file (misinformation_data_updated.xlsx).

## Usage

You can use this dataset for various purposes, including:

- Analyzing patterns of misinformation spread
- Studying user behavior on social media
- Developing algorithms to detect misinformation

## Disclaimer

Please note that this dataset contains simulated data and is intended for educational and research purposes only. The data should not be used for any commercial or malicious activities.