NLP

I couldn’t miss the fun Twitter hashtag #BadStockPhotosOfMyJob thanks to a tweet by Julia Silge and another one by Colin Fay. The latter inspired me to actually go and look for what makes a data science photo… What characterizes “data science” stock photos?

Do you know the Black Metal Cats Twitter account? As explained in this great introduction, it “combines kitties with heavy metal lyrics”. I know the account because I follow Scott Chamberlain who retweets them a lot, which I enjoy as far as one can enjoy such a dark mood. Speaking of which, I decided to try and transform Black Metal Cat tweets into something more positive… The Bubblegum Puppies were born!

Once again, a Twitter trend sent me to my R prompt… Here is a bit of context. My summary: Taylor Swift apparently plays the bad girl in her new album and a fan of hers asked a question…

Name a bitch badder than Taylor Swift 😍😛😤 pic.twitter.com/AkSyQBUIME

— Nutella (@xnulz) 10 de novembre de 2017

The tweet was then quoted by many people mentioning badass women, and I decided to have a look at these heroes!

I’ve recently been binge-reading The Guardian Experience columns. I’m a big fan of The Guardian life and style section regulars: the blind dates to which I dedicated a blog post, Oliver Burkeman’s This column will change your life, etc. Experience is another regular that I enjoy a lot. In each of the column, someone tells something remarkable that happened to them. It can really be anything.

I was thinking of maybe scraping the titles and get a sense of most common topics. The final push was my husband’s telling me about this article of Gabriella Paiella’s about the best Guardian Experience columns. She wrote “the “Experience” column does often touch on heavier topics”. Can one know what is the most prevalent “weight” of Experience columns scraping all their titles?

I don’t think rOpenSci’s Jeroen Ooms can ever top the coolness of his magick package but I have to admit other things he’s developped are not bad at all. He’s recently been working on interfaces to Google compact language detectors 2 and 3 (the latter being more experimental). I saw this cool use case and started thinking about other possible applications of the packages.

I was very sad when I realized it was too late to try and download tweets about the Eurovision song context but then I also remembered there’s this famous tennis tournament going on right now, about which people probably tweet in various languages. I don’t follow the French Open myself, but it seemed interesting to find out which languages were the most prevalent, and whether the results from the cld2 and cld3 packages are similar and whether they’re similar to the language detection results from Twitter itself.

One of my more or less guilty pleasures is reading The Guardian blind date each week. I think I started doing this when living in Cambridge, England for five months. I would buy i every weekday and The Guardian week-end every week-end. I wasn’t even dating at the time I discovered The Guardian blind dates but I’ve always liked their format.

I get so much into each date report that seeing both participants say they want to meet again makes me ridiculously happy. I like wondering how matches were made, but today I just want to look into the contents of post-date interviews.

It’s the second time I write a post about the blog aggregator R-bloggers, probably because I’m all about R blogs now that I have one. My husband says my posts are so meta. My first post was about R blogs names, in this one I shall focus on the last 1,000 tweets from R-bloggers.

As described in my last post, I extracted all notable deaths from Wikipedia over the 2004-2016 period. In this post I want to explore this study population. Who were the notable dead?

These last days a trending Twitter hashtag was “#actuallivingscientist”, whose origin can be find in this convo and whose original goal was to allow scientists to present themselves to everyone, a sort of #scicomm action. A great initiative, because we need science and we need everyone to know how it’s done, by actual human beings.

I didn’t tweet with the hashtag, but I consider myself a scientist with more or less experience in different fields – and my last post was about the scientist I married. In my timeline thanks to Auriel Fournier there were many tweets of ecologists studying animals. I’d like to say cute animals but some were carcasses… But still, it made me want to quantify which animals were the most present in the tweets. Any bet?