Methods Bites

Blog of the MZES Social Science Data Lab

Using Web Logs and Smartphone Records for Social Research

2020-04-14 13 min read instructionals [Ruben Bach]

How can social scientists collect and analyze web logs – records of individuals’ browsing behavior – for their own research? In this Methods Bites Instructional Blog Post, Ruben Bach summarizes some key insights of his talk in the MZES Social Sciences Data Lab in December 2019. The blog post discusses how to obtain and extract information from web logs and related data, shows how they can be used for social research, and concludes with a short discussion of how to handle big data extracted from web logs.

How to handle “big data”

Finally, a note on useful tools and prerequisites for analyzing web logs and records of smartphone use. First of all, the amount of data can quickly exceed the computational power of a standard desktop computer. Four months of web log data used in Bach et al. (2019), for example, contained about 38 million observations. Working with data of this size, researchers may have to consider using remote computing services like Digital Ocean, Amazon AWS or Microsoft Azure, which offer computational resources for little money through virtual servers. Second, understanding URL contents by observing single URLs is straightforward. Analyzing thousands of URLs, however, requires text mining and natural language processing (NLP) techniques if one wants, for example, to select only those URLs that point to news articles. Moreover, in addition to analyzing the title of a news article (which can often be observed from the URL alone), one might also want to analyze the whole content of the article. In such cases, in addition to being able to automatically extract the topic of an article through NLP techniques, knowing how to scrape website contents will likely also be helpful. Some useful materials are linked below.

About the presenter

Ruben Bach is a postdoctoral researcher at the University of Mannheim, focusing on social science quantitative research methods. His interests include topics related to big data in the social sciences, machine learning, causal inference, and survey research.


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