It’s become something of a truism in recent years that scientists in many fields find themselves drowning in data. This is certainly the case in neuroimaging, where even small functional MRI datasets typically consist of several billion observations (e.g., 100,000 points in the brain, each measured at 1,000 distinct timepoints, in each of 20 subjects). … Continue reading Neurohackademy 2018: A wrap-up
[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
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!