whocitedme 0.1.1

Google Scholar citation analysis tool for identifying high-impact citations, influential authors, and notable peers for grant applications and CV building.

WhoCitedMe

WhoCitedMe is a powerful Python library and CLI tool designed for researchers and academics. It automates the process of scraping Google Scholar citations, identifying who is citing your work, and analyzing the impact of those citations.

It goes beyond simple citation counts by enriching author data, matching missing Scholar IDs, and identifying the "top scholar" (highest-cited author) for each citing paper.

🚀 Key Features

• 📄 Citing Papers Scraper: Automatically scrape all papers citing a specific Google Scholar profile within a given year range.
• 🧩 Author Enricher: Handles truncated author lists (e.g., "J Smith, A Doe...") by parsing full citation data.
• 📊 Author Info Fetcher: High-performance, parallelized fetching of author metrics (Citation Count, h-index, Fellow status).
• 🆔 ID Matcher: Uses fuzzy matching logic to resolve missing Google Scholar IDs for citing authors.
• 🏆 Top Scholar Finder: Identifies the most influential author on every citing paper to help you understand who is citing you.

🎯 Use Cases

• Grant Applications: Demonstrate impact by listing high-profile researchers who cite your work.
• Tenure & Promotion: Provide detailed metrics on the quality of your citations, not just the quantity.
• Networking: Identify potential collaborators who are already building on your research.

🛠️ Installation

Prerequisites

• Python 3.10 or higher.
• Google Chrome installed (required for Selenium scraping).

📦 From PyPI (Recommended)

pip install whocitedme

💻 Local Development (using uv)

We use uv for fast dependency management.

1. Clone the repository:

   git clone https://github.com/KyanChen/WhoCitedMe.git
   cd WhoCitedMe
   

2. Setup environment with uv:

   # Install uv (if not installed)
   pip install uv
   
   # Create virtual environment
   uv venv
   
   # Activate virtual environment
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

3. Install in editable mode:

   uv pip install -e .
   

📖 Usage

You can use WhoCitedMe either via the command line interface (CLI) or as a Python library.

Command Line Interface (CLI)

The easiest way to run the tool is using the pipeline command, which runs all steps in order.

# Run the full analysis pipeline
whocitedme pipeline --user-id "YOUR_SCHOLAR_ID" --start-year 2018 --end-year 2024

# With custom output directory and worker count
whocitedme pipeline -u "YOUR_SCHOLAR_ID" -s 2018 -e 2024 -o my_output --workers 32

# Run in headless mode with proxy support
whocitedme pipeline -u "YOUR_SCHOLAR_ID" -s 2018 -e 2024 --headless --proxy http://127.0.0.1:7890

Individual Steps

If you prefer to run steps individually:

1. Scrape Citing Papers:

   whocitedme scrape --user-id "YOUR_SCHOLAR_ID" --start-year 2020 --end-year 2024 --output output/citations.csv
   
   # Run in headless mode (no visible browser window)
   whocitedme scrape -u "YOUR_SCHOLAR_ID" -s 2020 -e 2024 --headless
   

2. Enrich Author Data:

   whocitedme enrich --input output/citations.csv --output output/citations_enriched.csv
   
   # Start fresh (disable resume from previous run)
   whocitedme enrich -i output/citations.csv -o output/citations_enriched.csv --no-resume
   

3. Fetch Author Metrics (Parallelized):

   whocitedme fetch-authors --input output/citations_enriched.csv --output output/scholar_database.csv --workers 16
   
   # With proxy support
   whocitedme fetch-authors -i output/citations_enriched.csv --proxy http://127.0.0.1:7890
   

4. Match Missing IDs:

   whocitedme match-ids --citing output/citations_enriched.csv --scholars output/scholar_database.csv --output output/citations_verified.csv
   
   # With custom matching threshold (0-1, default: 0.7)
   whocitedme match-ids -c output/citations_enriched.csv -s output/scholar_database.csv --threshold 0.8
   

5. Find Top Scholars:

   whocitedme top-scholar --input output/citations_verified.csv --scholars output/scholar_database.csv --output output/citations_final.csv
   

Python API

For custom workflows, import the classes directly:

from whocitedme import (
    CitingPapersScraper,
    AuthorEnricher,
    AuthorInfoFetcher,
    IDMatcher,
    TopScholarProcessor,
)

# Step 1: Scrape citing papers
scraper = CitingPapersScraper(
    user_id="YOUR_SCHOLAR_ID",
    start_year=2020,
    end_year=2024,
    output_file="output/citations.csv",
    headless=False,  # Set True for headless browser
)
scraper.run()
scraper.close()

# Step 2: Enrich truncated author information
enricher = AuthorEnricher(
    input_file="output/citations.csv",
    output_file="output/citations_enriched.csv",
)
enricher.run(resume=True)  # Resume from previous run if interrupted
enricher.close()

# Step 3: Fetch author metrics (parallelized)
fetcher = AuthorInfoFetcher(
    input_file="output/citations_enriched.csv",
    output_file="output/scholar_database.csv",
)
fetcher.run(max_workers=16)

# Step 4: Match missing Scholar IDs
matcher = IDMatcher(
    citing_file="output/citations_enriched.csv",
    scholar_file="output/scholar_database.csv",
    output_file="output/citations_verified.csv",
    match_threshold=0.7,
)
matcher.run()

# Step 5: Find top scholars for each citation
processor = TopScholarProcessor(
    main_file="output/citations_verified.csv",
    scholar_file="output/scholar_database.csv",
    output_file="output/citations_final.csv",
)
processor.run()

See examples/basic_usage.py for a complete runnable script.

📂 Project Structure

WhoCitedMe/
├── whocitedme/
│   ├── __init__.py         # Package exports
│   ├── cli.py              # Command-line entry point
│   ├── scrapers/           # Web scrapers using Selenium
│   │   ├── citing_papers.py    # CitingPapersScraper
│   │   ├── author_enricher.py  # AuthorEnricher
│   │   └── author_info.py      # AuthorInfoFetcher
│   ├── processors/         # Data processing logic
│   │   ├── id_matcher.py       # IDMatcher
│   │   └── top_scholar.py      # TopScholarProcessor
│   └── utils/              # Helper utilities
│       ├── browser.py          # Browser driver creation
│       └── captcha.py          # CAPTCHA handling & random sleep
├── examples/               # Usage examples
│   └── basic_usage.py
├── output/                 # Default output directory (git-ignored)
├── pyproject.toml          # Project configuration and dependencies
├── LICENSE                 # MIT License
└── README.md               # This file

⚠️ Troubleshooting & Limits

• Google Scholar Rate Limits: If you scrape too fast, Google will block your IP.
  • Solution: The tool has built-in delays, but for massive jobs, consider using a VPN or proxy.
• CAPTCHA: If the scraper gets stuck, check the opened Chrome window. You may need to manually solve a CAPTCHA.
• Chrome Version: Ensure your installed Chrome browser matches the ChromeDriver version (usually handled automatically by undetected-chromedriver).

🤝 Contributing

Contributions are welcome!

1. Fork the repo.
2. Create a feature branch (git checkout -b feature/amazing-feature).
3. Commit your changes.
4. Push to the branch.
5. Open a Pull Request.

📄 License

Distributed under the MIT License. See LICENSE for more information.