There are thousands of articles on the web about web scraping and accessing web APIs. Most of them show you how to extract information from specific elements on a web page or how to communicate with a specific API in order to collect data. For smaller data collection projects, this knowledge may be sufficient, but large scale data collection which must run reliably over days or even weeks brings up additional problems that mainly focus on the robustness of the data collection process. I will try to tackle some of these problems in this post. I will use examples in Python, but the basic concepts can easily be translated to R or other programming languages.
Read More →Spiegel Online news topics and COVID-19 – a topic modeling approach
I created a project to showcase topic modeling with the tmtoolkit Python package: I use a corpus of articles from the German online news website Spiegel Online (SPON) to create a topic model for before and during the COVID-19 pandemic. This topic model is then used to analyze the volume of media coverage regarding the pandemic and how it changed over time.
National daily infection numbers clearly drive the volume of media coverage on COVID-19 during the observation period (January 2020 to end of August 2020) on SPON, which is probably not very surprising. Even though infection rates increased dramatically in the world in summer 2020 (e.g. in Brazil, India and USA), media coverage first decreased and then stayed at a moderate level, indicating that SPON doesn’t respond so much to rising infection rates at an international level.
You can have a look at the report here. All scripts are available in the GitHub repository.
A Twitter network of members of the 19th German Bundestag – part II
This is the second part about my project that deals with the Twitter network of members of the Bundestag. After getting the necessary data, which was explained in part 1, we will now focus on creating a network graph with links between the representatives’ Twitter accounts for exploratory network analysis.
Read More →A Twitter network of members of the 19th German Bundestag – part I
For the R tutorial that I gave at the WZB in the previous semester, I gave an introduction on how to query web APIs – specifically the Twitter API – and automated data extraction from websites (i.e. web scraping). I showed an example that combined both of these techniques for the goal of getting data about the Twitter activities of members of the current (19th) German Bundestag, which is the federal German parliament. The focus was especially on the question “who follows who” on Twitter. I thought it’s a nice little project showing how to use the Twitter API, do web scraping, combine the collected data and do some exploratory network analysis – all within the R environment. So I decided to polish the code a little bit, put in on GitHub and wrote two blog posts. The first part, i.e. this part, is all about getting the data.
Web scraping with automated browsers using Selenium
Web scraping, i.e. automated data mining from websites, usually involves fetching a web page’s HTML document, parsing it, extracting the required information, and optionally follow links within this document to other web pages to repeat this process. This approach is sufficient for many websites that display information in a static way, i.e. do not respond to user interaction dynamically by the means of JavaScript. In these cases, web scraping can be implemented with Python packages such as requests and BeautifulSoup. Even interactive elements such as forms can be emulated by observing the HTTP POST and GET data that is send to the server, whenever a form is submitted. However, this approach has limits. Sometimes, it is necessary to automate a whole browser in order to implement web scraping on JavaScript-heavy websites as will be shown with a short example in this post.
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