In defense of Facebook

[UPDATE July 1st: I’ve now posted some additional thoughts in a second post here.] It feels a bit strange to write this post’s title, because I don’t find myself defending Facebook very often. But there seems to be some discontent in the socialmediaverse at the moment over a new study in which Facebook data scientists … Continue reading In defense of Facebook

estimating the influence of a tweet–now with 33% more causal inference!

Twitter is kind of a big deal. Not just out there in the world at large, but also in the research community, which loves the kind of structured metadata you can retrieve for every tweet. A lot of researchers rely heavily on twitter to model social networks, information propagation, persuasion, and all kinds of interesting … Continue reading estimating the influence of a tweet–now with 33% more causal inference!

The homogenization of scientific computing, or why Python is steadily eating other languages’ lunch

Over the past two years, my scientific computing toolbox been steadily homogenizing. Around 2010 or 2011, my toolbox looked something like this: Ruby for text processing and miscellaneous scripting; Ruby on Rails/JavaScript for web development; Python/Numpy (mostly) and MATLAB (occasionally) for numerical computing; MATLAB for neuroimaging data analysis; R for statistical analysis; R for plotting … Continue reading The homogenization of scientific computing, or why Python is steadily eating other languages’ lunch