I flicked over a Google research paper last week that I think some of you might find interesting. In the study researchers analyse sharing and its relationship to a video’s popularity, and while the whole paper is worth a read, I found the discussion on the ‘socialness’ of popular videos to be the most interesting.
The key takeaways from the discussion (section 6.1 if you’re interested) were:
1) Not all popular videos are highly social
2) Most videos become popular on YouTube through search and related videos (not through sharing/referrals).
3) Viral videos rarely make it into YouTube discovery mechanisms such as search/related videos.
3.1) The data suggests the way YouTube computes related videos does not apply well to viral videos.
If you want to read more check out the full paper.
Around a year ago I opened an account on Buzzfeed with the goal of shaping a post that would make the front page, and I succeeded on my first attempt after spending an hour browsing cat videos on YouTube.
It’s hard to draw any wide reaching conclusions from that post, but I still think it hints at a few truths about Buzzfeed and its audience… truths that Jonah Peretti has spent a lot of time and effort obscuring.
Truth 1: Buzzfeed likes to build up mystery and hype surrounding how it writes for its audience, but the cold hard truth is that filtering through a ‘cat videos’ search on YouTube (my exact keyphrase) is sometimes enough to garner 20K+ views and make it onto the homepage.
Truth 2: Data science and research has helped define broad categories that work on Buzzfeed, but there’s no algorithm that dictates its content. Buzzfeed has not ‘cracked the code’ for making viral content… it relies on guesswork and creativity (within well-researched boundaries) to produce its content. Sometimes it works and sometimes it doesn’t, and this is only slightly different to how traditional newsrooms have worked since time immemorial.
I’m not saying it’s always this easy, and compared to the staff posts my cat-post metrics are modest to say the least. But I think Peretti’s rhetoric about the science behind Buzzfeed needs to be recognised for what it is, which is nothing more than a marketing pitch. What worked about my post was not that it had the backing of Buzzfeed’s viral magic, but that it was fresh content that hit the buttons of a niche audience (luck also plays a big part). It’s not easy but it’s not rocket science, and it’s not something that Buzzfeed does better than anyone else.
I’ve been working on data mining Twitter for a while now, and while it’s taken a fair amount of blood, sweat, and tears… the results have been worth the effort. Below is a snapshot of the interactions between ~2,000 Twitter users over seven days. It maps out a week of discussion on the #BBAU hashtag (from 2012) and you can explore the full dynamic map online here (it takes a minute or so to load).
This visualisation won’t mean much to you unless you watched the show, and it’s still not completely finished. It’s a good proof of concept though, and as a close watcher of the community I was amazed how much more sense it made when looking at it like this.
It’s fascinating to see the how users cluster around influential users to form micro-communities within the broader picture. It goes without saying that this kind of visualisation has great potential, and aside from that it looks pretty cool :).
I’ve put together an interesting map showing the overlap between native title claims and mining operations. The map is based off data from Geoscience Australia, and it shows active mines, deposits, and historical operations on one layer, and native title claims on the other (you can click on points of interest to get more info).
It’s interesting to note the extent of active native title in Australia. Also of interest are the claims that extend into the ocean above Cape York.
With it’s multiple layers and datasets, this visualisation is starting to test the limits of what Google Maps can achieve. Pretty cool stuff.