My journey into text mining
My journey into text mining started when the institute of Digital Humanities (DH) at the University of Leipzig invited students from other disciplines to take part in their introductory course. I was enrolled in a sociology degree at the time, and this component of data science was not part of the classic curriculum; however, I could explore other departments through course electives and the DH course sounded like the perfect fit.
Turning COVID-19 into a data visualization exercise for your students
We will emerge from this pandemic with a better understanding of the world and an improved ability to teach others about it. For now, we need to be continuously analyzing the data and thinking about the lessons we can learn and apply. Here’s how you can join in!
At SAGE, we have been working with academics around improving and sharing teaching resources, especially for quantitative and computational methods in social sciences. Besides the mass remote and emergency teaching experiment happening right now, one of the positive things we can already identify and reuse to improve learning in methods courses is the glut of data visualizations. The absolute advantage here is that all these visualizations are produced (almost always) with the same raw input, telling a variety of different stories. What better way to explain the different uses and impact of visualizations and the use of different tools to students than examples based on the same data?
From preprocessing to text analysis: 80 tools for mining unstructured data
Text mining techniques have become critical for social scientists working with large scale social data, be it Twitter collections to track polarization, party documents to understand opinions and ideology, or news corpora to study the spread of misinformation. In the infographic shown in this blog, we identify more than 80 different apps, software packages, and libraries for R, Python and MATLAB that are used by social science researchers at different stages in their text analysis project. We focused almost entirely on statistical, quantitative and computational analysis of text, although some of these tools could be used to explore texts for qualitative purposes.
Collecting social media data for research
Human social behavior has rapidly shifted to digital media services, whether Gmail for email, Skype for phone calls, Twitter and Facebook for micro-blogging, or WhatsApp and SMS for private messaging. This digitalization of social life offers researchers an unprecedented world of data with which to study human life and social systems. However, accessing this data has become increasingly difficult.