Last week – April 25 – Facebook posted the 2018 first-quarter data on revenues and users (here the original post from Facebook.) The perhaps unexpected take-home message is that, in spite of the Cambridge Analytica data scandal, everything seems to go well with the social media. In particular, monthly users continued to grow at the expected rate (see the graph below – original here).
Few thoughts on an important paper that just appeared in Science, The spread of true and false news online. The paper received (and will receive) justified attention: it is massive (“~126,000 rumor cascades spread by ~3 million people more than 4.5 million times” in a long temporal window – from 2006 to 2017), it includes several detailed analyses (the authors did not only check basic metrics such as speed and size of diffusion, but they measured things like structural virality; the proportion of political versus non-political news; the role of bots; they run a sentiment analysis of the tweets, etc.), and it has a straightforward (and I guess welcome to many) take-home message: “fake” news are more successfull than “true” news in social media, at least in Twitter (*).
In 2013 I took the time to collect some data about the spread of a story about Oreo cookies. According to it, scientists demonstrated that Oreo cookies are as much, or possibly more, addictive than cocaine. The story spread and faded very quickly, in a couple of days in October 2013, but was reported by hundreds of English language media outlets, including prominent ones such as the Huffington Post or the Guardian.
I am almost leaving after few intense days at the Second Moscow-Tartu Late Summer School on Digital Humanities, where I was invited to give a talk about my research on the cultural dynamics of emotion in fiction (see two papers here and here), and more recently in song lyrics (no paper yet – a blog post documenting the very beginning of the research is here).
I found, thanks to twitter-induced serendipity (others call it procrastination), the lyrics of the songs included in the annual Billboard Top-100 from 1965 to 2015 (i.e., considering a few missing, ~5,000 songs). You can find in GitHub, together with the raw data, some clarifications on how the data were collected, their limitations, etc. plus a pointer to a nice analysis already done.