crawls_nest

• Background
• Working with Common Crawl data
• Usage
  • File collection
  • File processing
  • Requirements

Repository hosting code to access the Common Crawl from your own machine 🥳

...work in progress

NB: This Readme file is intended for users! If you would like to contribute to this repo, please refer to the guide for contributors

Background

The code builds on the warcio library for fast WARC Format reading and writing.

Working with Common Crawl data

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crawls_nest depends on the webrecorder / warcio library to read records and content of WARC files. Please see the warcio documentation for usage instructions of the library.

Usage

For ease of usage, clone the repository via

$ git clone https://github.com/giuliaok/crawls_nest.git

File collection

The columnar_explorer.py script requires two arguments: month and year, which serve to define the chunks of the crawl we want to work with. We provide two further optional arguments: domain defines a specific domain to extract from the crawl (e.g. .com, .org). If the domain is unspecified, the script will extract all websites from the crawl. clean subsets the columnar index by removing some often empty features of the crawl. The argument is True by default. Below is an example of how to use the script from the command line:

$ python columnar_explorer.py --month august --year 2022 --domain org

File processing

The process_warc_files.py script requires one argument: data_directory, which is a path to the dataset containing the files extracted in columar format through columnar_explorer.py (or whichever other script you may want to use). We provide five further optional arguments: get_html, which is False by default, to extract html hyperlinks from wat records. get, which is used to extract and process text from wet records, geography which defines the geography of choice when searching postcodes within a record's text, if left empty all postcodes are extracted, classification which defines whether we want to provide an initial industrial classification for each website and finally saving_freq. This last argument is quite important as it defines after how many processed records to save a copy of the results: these datasets tends to be incredibly big and it is advisable to work in batches. Below is an example of how to use the script from the command line:

$ python process_warc_files.py --data_directory data_directory --get True --geography London --classification False --saving_freq 

Requirements

This project is based on python==3.10.8 but should work on previous versions as well after 3.7. The dependencies are as follow:

transformers==4.36.0
requests==2.28.2
gensim==3.8.1
warcio==1.7.4
numpy==1.24.1
beautifulsoup4==4.12.0