🚨 Publication alert: Android log data are increasingly used in research, so @dougaparry and I have developed the first step-by-step tutorial on how to actually extract behavioral metrics from them. Read it now in Computational Communication Research:

This paper is supposed to help researchers handle raw Android log data in a systematic manner and facilitate access to this rich source of information 🔑. Feel free to use and share it. Feedback is very welcome!

The supp. material includes a working rendition in R code, an example data set with raw Android log data, and the data set that results from applying the code to it. There are also lists that helps identify background processes and app names 📋 - with a script to retrieve them.

This primer first elaborates on the potential of and difficulties when handling raw Android log data 📊. Then, the process of extracting glances, sessions, and episodes of use is described in a hands-on guideline with pseudo-code to follow along 👩‍💻.

🦋 I've moved to Bluesky - follow me there!

Where I write my reviews

Want to collect TikTok data but need help figuring out where to start? Look no further but here 👀: https://t.co/JxAxNLKayZ @JWI_Berlin

when you get a tough question after your talk but you have the perfect supplementary slide

Academic ironies

Check out this blog post on a preprint article providing a tutorial for extracting use metrics from raw Android log data, created by @dougaparry and myself: https://t.co/N6AMF53rgv It is worth a read for anyone working with such data 👩🏾‍💻!

Why numbers are sometimes better than words. What people think you say when you say “little chance,” “probably not,” or “highly likely.”

Students at Stanford have built alphaXiv, an open discussion forum for arXiv papers. Think of it as 𝕏 for research.

🚨 New preprint! @dougaparry, @mjemmer, and I investigated the times, gratifications, locations, and activities alongside smartphone and app use in Germany 📱. To do so, we used a combination of surveys, MESM, logging, and data donations. https://t.co/xhpSgZlZIF

Me presenting my research

I'm European. I recently visited the USA for the first time since 2018, hitting up Las Vegas and New York City. What I witnessed left me stunned. 15 American oddities I still can't wrap my head around:

When somebody cites my paper

Can’t make this up, my in-flight tv is already hacked on the way to defcon

We can answer many questions with Android log data, but extracting useful metrics of *human* smartphone usage from the raw event log is not easy. Here, we present a primer (with code) on how to do this. Any and all feedback appreciated on this working paper!

This paper is supposed to help researchers handle raw Android log data in a systematic manner and facilitate access to this rich source of information 🔑. Feel free to use and share it. Feedback is very welcome!

The supp. material includes a working rendition in R code, an example data set with raw Android log data, and the data set that results from applying the code to it. There are also lists that helps identify background processes and app names 📋 - with a script to retrieve them.