dredmorbius, 1 year ago One of the challenges of having an Eminently Queryable Data Trove is ... deciding what to query it about. I've long thought that HN was fairly obsessed with various aspects of the hiring process, from both employer and worker perspectives. Let's check that ... $ egrep -i '(interview|hiring|recruiting)' <(grep '^ Title:' parse.log ) | wc -l<br></br> 1282<br></br> Ayup. That's 1,282 stories out of 178,072, or just over 0.7%, but still a healthy chunk. By contrast, "housing" gets 90 hits, "Tesla" 413, and "Musk", 114. Or the FAANG+M set: Facebook: 2,414<br></br>Apple: 2,495<br></br>Amazon: 1,467<br></br>Netflix: 326<br></br>Google: 5,900<br></br>Microsoft: 1,523<br></br> I'm still trying to sort out a way to search / determine "statistically interesting terms", that is words or phrases which are disproportionately represented in submission titles. #HackerNewsAnalytics #hiring #interviews #recruiting
One of the challenges of having an Eminently Queryable Data Trove is ... deciding what to query it about.
I've long thought that HN was fairly obsessed with various aspects of the hiring process, from both employer and worker perspectives.
Let's check that ...
$ egrep -i '(interview|hiring|recruiting)' <(grep '^ Title:' parse.log ) | wc -l<br></br> 1282<br></br>
Ayup.
That's 1,282 stories out of 178,072, or just over 0.7%, but still a healthy chunk. By contrast, "housing" gets 90 hits, "Tesla" 413, and "Musk", 114.
Or the FAANG+M set:
Facebook: 2,414<br></br>Apple: 2,495<br></br>Amazon: 1,467<br></br>Netflix: 326<br></br>Google: 5,900<br></br>Microsoft: 1,523<br></br>
I'm still trying to sort out a way to search / determine "statistically interesting terms", that is words or phrases which are disproportionately represented in submission titles.
#HackerNewsAnalytics #hiring #interviews #recruiting