My posts are usually R only. But in this post, I want to talk about a suite of tools developed by my colleagues and me that goes beyond R only. This suite of tools helps to gather results from different search engines and includes a browser extension to scrape the results, and a Python library and an R package to parse the results.

The core tool is a browser extension for Mozilla and Chrome that simulates a user searching (at least) 50-top main, news, images and videos search results of up to 8 different search engines.

The repository is well documented and walks you through the steps of setting up the extension to scrape data. Always make sure to deactivate the extension once you are done since it interferes with your normal browsing.

The extension can store local snapshots of the html files of the search results for later analysis. This is done via the web extension SingleFile. To parse these html files efficiently for important information, we a Python library as well as an R package.

To use the Python library, simply clone the repository and either add webbotparser/webbotparser.py to your working directory or navigate to the folder, and run

The webbotparser is then available in your Python installation.

(The following is an excerpt of the repository README)

For the search engines and result types supported out of the box, simply run

and initialize the WebBotParser for the search engine and result type your are investigating, for example

Then, you can obtain the search results as a pandas DataFrame and metadata as a Python dictionary with

Furthermore, parser.get_metadata(file) can be used to only extract the metadata. parser.get_results_from_dir(dir) allows to directly extract search results spread over multiple pages, as Google text result are provided for instance. For examples also see example.ipynb.

WebBot archives images inline in the html file of the search results, i.e., they are neither external files on your drive nor fetched from the original source on viewing the downloaded search results page. This allows us to extract the images directly from the html file for further analysis. The engines and result types supported out of the box with WebBotParser allow for extracting images as well. Simply initialize WebBotParser as follows:

You can optionally specify extract_images_prefix, extract_images_format, and extract_images_to_dir. See example.ipynb for more details, including preview in Jupyter Notebooks.

WebBotParser out of the box only provides support for some search engines and result types. Even these parsers might stop working if the search engine providers decide to change their layout. However, WebBotParser can still be used in these cases by defining a custom result_selector, queries, and optionally a metadata_extractor function. In this case, a WebBotParser is initiated with these instead of with the engine attribute

Under the hood, WebBotParser uses BeautifulSoup to

See the below example for available types of queries and their usage

You can optionally provide a metadata_extractor(soup, file) function to extract metadata alongside the search results, or import one of the existing extractors, e.g. with

The R package can be installed from GitHub

The package contains an example html from a google search on climate change.