Social Media Text Scraping

I need a reliable solution that automatically pulls public-facing text from specific social-media websites and delivers the content back to me in a clean, structured format ready for downstream analysis. The task covers three steps: building or customising a scraper in Python that navigates the chosen platforms, capturing posts, comments and any associated meta-information I specify; cleaning the raw output to remove emojis, markup, duplicate lines and other noise; and packaging the final, deduplicated dataset as a CSV or JSON file. Please write the code so I can rerun it anytime (command-line script or Jupyter notebook is fine) and include concise setup instructions plus brief inline documentation. I expect respectful rate-limit handling and compliance with each platform’s public-data policies. Acceptance will be based on: • Accurate capture of the requested text fields from the sample profile list I provide • Fewer than 1 % duplicate rows after cleaning • Script runs end-to-end on my machine with only standard Python libraries or clearly listed open-source dependencies If you already have experience scraping social platforms via Selenium, BeautifulSoup, Scrapy or similar tools, that would be ideal.

Project ID: 40385875