Watch a Union Metrics Reblog Tree Grow:
I tracked a week’s worth of reblogs on a single post, thanks to Union Metrics. This animation shows a series of reblog trees, growing over time as reblogs spread the post across Tumblr. The single circle on the left is my original post.
When reblogs occur, new circles appear to the right of the original post circle. As others reblog, more circles crop up, growing from left to right. The larger the circle, the more reblogs came from that blog. The top three amplifying blogs were socialgoodness, analyticisms and unionmetrics. Thanks for all the reblogs (and likes)!
The animated GIF really ought to be used more as a reporting tool - it’s a way of showing change over time in something that’s SO much simpler & more manoeuvrable than video.
I also want to build myself one of these - either through Union Metrics (I think I have a personal account?) or manually, for the exercise.
Could be a really useful tool to identify influencers and hubs in the street-goth account I’m seeking to grow.
Six Degrees Of Health Goth
So I took my hand-sketched semantic network map from yesterday and did it again, properly.
1. Start at health goth, and look at the 3 related topics that Tumblr gives you. Write these down as sets of pairs:
health goth - street goth
health goth - seapunk
health goth - vaporwave
3. Ladder up from the 3 related topics, looking at each of their related topics. Do this process 6 times, building a list of pairs.
4. Import the dataset as a .csv file into Gephi as a list of directed edges.
5. Force Atlas 2 layout, weight node size by in-degree, and node colour by modularity.
6. Tweak node colour a bit manually so the different communities show up better.
Working assumption: Tumblr’s related tags are based on tags co-occuring in the same post. Consequently it’s based on user behaviour (not say hand of mod), and we can look at how tags are connected as a proxy for how Tumblr users think these ideas & concepts are related.
The relationships between tags loop round and keep interconnecting:
health goth —> seapunk —> grunge —> vintage —> retro —> hipster —> girl —> women’s fashion —> chic —> korea —> women’s fashion
Where a group of tags keep linking into each other - e.g. one around women’s fashion, chic, street style, korea, china and girl - that forms a community (in network terms). The modularity algorithm can detect which groups of tags are most closely linked like this.
1. 79 tags are within 6 degrees of health goth. Given that each tag has 3 related tags, that shows there’s a lot of interconnection between them (we could potentially be dealing with I think 364 tags: 3^0+3^1+3^2+3^3+3^4+3^5).
2. Health goth itself has in-degree of zero, aka none of the tags it links out to ever link back to it. This suggests it’s a small subculture
3. Vaporwave has the highest in-degree, of 6. Now, this is partly because it’s close to our starting point so it has more opportunity to gain “ins” - but moreso because it’s the hub for a number of different design/art aesthetics: net art, web art, webpunk, seapunk, the word ‘aesthetic’ and of course health goth itself.
4. HBA links round to itself - ok strictly speaking hba links to hood by air, but I aggregated the two to get a nice loop :0
5. Korea and China are huge tags around women’s fashion and chic. This is massively interesting. I’m seeing it around streetwear and street goth too, though it doesn’t show up in this map. As a teenager I grew up on the FRUiTS magazine books of streetstyle from Tokyo… But it’s now looking like Seoul’s stolen Harajuku’s thunder.
6. Women’s fashion is correctly punctuated, but mens fashion, mens clothing and mens style are not.
1. Have lots of followers.
2. Have more followers than people you follow.
3. But don’t look like you’re trying to get followers by hashtagging too much, etc.
4. Don’t serial post. (“You only want to post one Instagram a day.”)
5. If you do post multiple things per day, they’d better be amazing. (“You can post multiple tweets a day, but they can’t be stupid or not interesting.”)
6. If you game the system, don’t get caught. (“She [my friend] probably has 20 fake accounts where she goes and likes her own pictures.”)
7. Remove photos that don’t get enough likes.
8. Be witty. (“Cute and clever captions are important. People judge you if they’re weird.”)
9. Time your posts for optimal like-getting. (“There’s a lot of social pressure to get likes, so you have to post it at the right time of day. You don’t want to post it during school when people don’t have their phone.”)
10. Facebook is for photos that weren’t good enough for Instagram.
Source: focus group of 12 girls aged 13-24 conducted for WeHeartIt.com.
Published in Time magazine by Katy Steinmetz, "Teen Girls Describe the Harsh Unspoken Rules of Online Popularity"
The article and press release focus on the pressures that teenage girls feel while using social media - bullying, trying to fit in, and the pressure to be perfect.
But I think it’s also interesting to understand how teenage girls imagine their social worlds and how they understand their social lives being influenced by the context of the technologies they use. What heuristics do they create to make this managable and understandable? Well, these.
And, well - I’m 5-10 years older than the girls focus-grouped, but these are pretty much my rules of social media too. Not just personally. They’re rules I’d tell clients - timing matters, visual quality is crucial for Instagram content, and don’t use hashtags #like #an #amateur.
So you see it’s not (solely) teenage girls and their social pressures driving this pursuit of followers and likes, but something built into the architecture of our social networks themselves. They’re what these platforms give us to work with - they’re fairly thin platforms for interaction, allowing a limited range of gestures. But we’re able to inscribe these tiniest of signals - not just likes, but ratios, timings, frequencies - to stand for broader social norms nonetheless:
It’s important for people to like you.
You should pretend that this is effortless: ‘trying too hard’ exposes us all, and is thefore taboo.
Appear humble - tall poppies get cut down.
Humour is the most socially acceptable way to stand out.
We’re such herd animals.
The Internet in Real-Time
Online data visual presents how much activity that occurs on familiar online apps and services.
I like Matt Muir’s comment from his weekly Web Curios roundup (recommended, btw):
PRO-TIP - keep this in the background next time you have a meeting with a client in which they demand that you make CONTENT for them, and just look at it significantly when making the point that the terrible likelihood is that NOONE WILL CARE BECAUSE LOOK AT ALL THE OTHER THINGS.
"Twitter and Facebook may need to roll out a new sales pitch. The two social networks have spent the past year trumpeting a virtuous cycle between people watching television and using social media. But, in spite of the buzz, NBCUniversal’s head of research Alan Wurtzel says that social media “is not a game changer yet” in influencing television viewing."
A few notes:
1. This was a study of the Sochi Olympics - don’t expect the same results to apply for TV dramas, breaking news, or talk/discussion type shows.
2. "during the 18-day period of coverage, just 19 per cent of Olympic viewers posted about the games on social media" - well, yes. This is because we are free to talk about whatever we want on Twitter and Facebook, and there’s really a vast range of things to talk about. No one topic is ever universally discussed. This isn’t a failing, but rather a simple fact about the kind of enormous wide-open possibility space that social media is. Companies can’t dominate it top-down.
3. We need to think about what else social might do, rather than solely measuring it by increasing overall viewing figures. Can it drive loyalty, i.e. does it keep people watching the whole show rather than switching off? Might it stop viewers dropping off from episode 1 to episode 2? Does it keep people on the sofa for longer, and thus more likely to see more ads? Does it distract from attention on the TV screen and thus reduce ad recall? Does it keep people subscribing to premium cable channels so that they’re able to watch the talked-about shows, whether or not they actually view them? There are a whole multitude of ways that we might explore to find impact.
4. "Twitter has pushed new advertising products that link ads on television to promotions on its site, for example, by targeting people on Twitter who are likely to have seen the commercials on television. And early results show that such targeted campaigns can increase the effectiveness of television commercials." Seeing an advert twice has greater impact than seeing an advert once shocker.
Still - while advertising ultimately funds the whole merry-go-round - the interesting bit is the expanded, cross-platform user experience of the show. People aren’t just being entertained by the TV programme itself, but a wider interactive social ecosystem around it. I don’t mean to present this as a huge break - people have been being social and discursive around TV since year dot, i.e. you watched it with other people in your living room, and you talked about it with them. What’s new is that the discussion is more public, and there are opportunities for new feedback loops and interactions, for multi-dimensional networked narratives.
We see this happening in Dr Who already, as the writers listen in on fan feedback - or in the way the BBC created in-character blogs for Sherlock Holmes, Dr Watson and Molly Hooper. Fan-fiction and fan media is the other side of it, of course, as fans re-caption GIFs and re-tell stories, and ultimately spend much more time with the secondary media than the source material. They’re streets ahead of the TV networks in the quality and imagination of the content they’re producing - and networks could emulate and learn from that.
So is the business opportunity just to show the same ad on Twitter as on TV? Or is it to tell your story across TV, text, GIFs, bonus video, blogs and Instagram… To create something that claims eyeballs for a lot longer than 1hr/week, and then - oh ok - sell advertising against the lot of it?
(What with consumer incomes having stagnated & housing costs rocketing, selling more advertising must surely be a game of diminishing returns seeing as people can’t afford to buy net more stuff - only switch their current spending. But that’s a post for another day…)
How do videos go viral? How do people share them through social networks? And what are the dynamics of ‘virality’?
Following the success of our Gangnam Style vs. Harlem Shake study (May 2013), Twitter UK invited us to explore four more big viral phenomena. The stories we selected have all been driven by video, and have been chosen to represent various types of video content:
- Commander Hadfield singing Bowie’s “Space Oddity” on the International Space Station (music)
- Dove “Real Beauty Sketches” for advertising (the most-watched advert EVER on YouTube)
- Ryan Gosling Won’t Eat His Cereal series of Vine videos, for serialised narrative content and mobile
- A grass-roots video of June’s protests in Izmir, Turkey, to provide an international and news dimension
Turns out there’s not a single model of virality. Instead, different types of videos spread in different ways. Different types of content appeal to different audiences and the structure of these audiences is what shapes the viral diffusion.
Understanding the dynamics of that spread – quantifying it using metrics, and digging into the influencers and demographics to understand some of the “how”, is what we’re going to talk about in this series of blog posts.
But first, take a look at the diffusion maps below, which show the pattern of tweets and retweets for each video (click to embiggen). It’s immediately clear that there’s something different going on for each. Some, like Commander Hadfield, have one big hub (Hadfield himself) driving half or more of the sharing. Others like Dove Real Beauty and the Turkish protest video show a constellation of many smaller influencers, each being reblogged by smaller groups. Read on and we’ll explain why.
(Blue nodes = tweeters. Yellow nodes = retweeters. Size = author visibility, i.e. estimated reach).
What we did
We used Pulsar’s content-tracking technology to collect and analyse any tweet containing a link to the videos we were tracking and understand how they were shared on Twitter. What we’re studying is content diffusion and content discovery – the way videos are shared, recommended, and retweeted until they become viral phenomena. Of course people share content in other ways too – not least on Facebook – and YouTube search is in fact the second biggest search engine in the world, after Google. But Twitter provides the strongest dataset for analysis, and its role as a “hub” for curating content from across the whole social web makes it an apt case study.
By me and Fran (@abc3d) of FACE, using our social media research platform Pulsar (PulsarPlatform.com). As presented at Strata, the big data conference, today. And there’s Part 2 to follow next week, digging deeper into how this content is spread through social networks.
How Videos Go Viral On Twitter
This network map video shows how people shared the Dove ‘Real Beauty Sketches’ video on Twitter.
We also analysed how the Commander Hadfield ‘Space Oddity’ video spread. Cooler subject, but sadly it made for a slightly less awesome diffusion video because it went viral so quickly. All the secondary nodes (influential sharers like Dara O’Brien and William Gibson) had tweeted it within 2 hours of the video’s release, meaning that the network’s pretty static from then on.
But our video did tweeted by Commander Hadfield himself, which was nice.
I am speaking at a thing:
On: Sunday 12th May, 6pm
At: Auto Italia South East, London N1C 4AE
The explosion of social networks means that the whole richness of human interaction - fights, breakups, love, loss, work, politics, art - are played out across network space by large numbers of the human population. Simultaneous with this is the rise of “Big Data” - huge data sets drawn from these networks that allow researchers (states, the public and private sector) to learn much about the populations under study. Virally and memetics, allegedly spontaneous phenomena can be analysed with the statistical gaze. And everything else.
Alex Andrews is joined by Jay Owens and Ed Manley to ask what does all this mean? What does it mean to be intensively connected like this - selves porous and attached at every waking moment, blurring the boundaries of self-performance work and leisure? How do the micro-banalities of every day life - from the daily commute to the walk in the park - play out on a vast aggregate macro level of Big Data? What is it to have a self on a social network, a data self?
I’ve written a new piece on my work blog:
"Attention marketing" is a current buzzword - and a big problem. In a world of near-infinite media abundance, getting anyone’s attention becomes a profound challenge. However much effort you put into creating content, you can’t guarantee it’s going to get an audience.
But brands and media agencies have come up with a solution. They’ve learnt to listen, to wait and react. Call it real-time marketing, call it culture-jacking – or simply call it “learning from Oreo” and their megawatt SuperBowl tweet in February that went viral. Let the audience grow itself – then tap into that. Any news event will do – maybe.
When former British PM Margaret Thatcher died, some brands immediately tried to jump on this bandwagon. This is what happened…
CHART OF THE DAY: The Difference Between What You Share And What People Want To Read
People are sharing science related content more often than other content category. However, science content is clicked on significantly less than the content that is less popular for sharing, like politics, news, or celebrity gossip.
There’s some rationale behind the split in what’s read and what’s shared. People will want to share science content because it makes them seem smart. People will click on celebrity content because no one will know.
Full Story: Business Insider
Slightly surprised politics & news are so heavily clicked-on - surely politics suffers from the same “Share because it makes you look smart, but secretly find a bit boring” issue that science & tech do?
This is awesome;
“Rubbish, reckon, blimey”
Read the full paper here: Word usage mirrors community structure in the online social network Twitter, by John Bryden, Sebastian Funkand Vincent AA Jansen. EPJ Data Science 2013, 2:3
The significant part of the article:
Royal Holloway has now filed a patent application to secure the intellectual rights to many of the techniques used in the research, which staff hope could have commercial applications. “There are numerous applications of our method, including social group identification, customising online experience, targeted marketing, and crowd-sourced characterisation,” the paper adds.
Here’s the patent application: WO2012/080707
So they publish in an open-access journal, and then try and lock down the method? That feels like a nasty move - I know nothing at all about patent law, but it’s fairly clearly a patent on the analysis method, not a proprietary commercial application. So what, any other researchers wanting to use these methods would not be allowed, or have to pay a fee? Fuck that - imagine if the chemists patented titration…
I’d also like to know about just how original their method really is - again, I’m certainly not an academic network analyst, but I get the gist of what they’re doing and it seems perhaps a unique combination of surely fairly standard network analysis moves? Perhaps it hasn’t been patented already because the compsci community know better than to lock down methods - but then along come a bunch of biologists…
Anyway, if you know more about this (Alex Leavitt, looking your way) I’d be very interested to hear your thoughts.
Final thought: it’s pretty old data - tweets collected in December 2009 from as far back as 2007. These communities do not necessarily exist now - pretty naff to use the word “tweetup” at SXSW this year, right? And the foodies are into kale now, not chard!
Seven seminal dilemmas about moral behaviour on the internet, from 1993 to today.
Featuring trolls, addicts, anonymity, suicide, community, and big data.
On my longform blog - Tumblr, pray forgive me! ;)
As Susan Sontag wrote in On Photography,
there is something predatory in the act of taking a picture. To photograph people is to violate them, by seeing them as they never see themselves, by having knowledge of them they can never have; it turns people into objects that can be symbolically possessed.
Sontag notes that this makes for a nostalgic gaze, an understanding of the world as primarily documentable. For those who live with status updates, check-ins, likes, retweets, and ubiquitous photography, such an understanding is near inescapable. Social media have invited users to adopt a sort of documentary vision, through which the present is always apprehended as a potential past. This is most triumphantly exemplified by Instagram’s faux-vintage filters.
There’s always tension between experience-for-itself and experience-for-documentation, but social media have brought that strain to its breaking point. Temporary photography is in part a response to social-media users’ feeling saddled with the distraction of documentary vision. It rejects the burden of creating durable proof that you are here and you did that. And because temporary photographs are not made to be collected or archived, they are elusive, resisting other museal gestures of systemization and taxonomization, the modern impulse to classify life according to rubrics. By leaving the present where you found it, temporary photographs feel more like life and less like its collection.
I replied to a question on Quora a few months ago - What are the *unofficial* rules of Tumblr (and what ought they to be)?
"Conventions, courtesies, credits, comments — what are the spoken and unspoken rules of the Tumblr community? Where is the community deficient and where does it excel?"
Here’s my take:
1. Be interesting
Have a point of view and add something to your posts - pull out what’s interesting about the argument, connect it to another idea, or just explain why you like it. Give people a hook to engage with to inspire questions or reblogged responses
2. Be honest
Text-based Tumblr likes personal accounts, confessionals and the exploration of personal vulnerability. (It’s the closest thing to the Heir To LiveJournal out there.) That doesn’t have to be your thing, but it’s definitely not a bragging, self-promoting kind of place.
3. Promote your friends
If a Tumblr or Twitter friend has written something good, give them a boost. Pro-social behaviour is good for community-building.
4. Credit your sources
Not widely done, but should be. Use both the linked URL and the body text to credit who made the image, what it’s called, and their website. (Don’t know? Use Google Image Search or TinEye.com.
5. Don’t be an asshole
Tumblr’s probably more sensitive towards sexism, racism, transphobia etc than other communities. Being a bigot isn’t cool.
ETA: Imperfectly phrased. Obviously don’t be a bigot because it’s wrong and hurtful, not because you’re concerned with “cool”. But, yes, community norms mean you ain’t going to be cool on Tumblr if you’re a hater.
What do you reckon?
A long tail of bots, read-onlies and the inactive - you can see as well as I do that the majority of likes & reblogs I get are from the same core of 20 people (thanks, guys ;). Hell, I got 50 follows this afternoon but only 5 of them were “real” (?) enough to show up in my notifications page… Weird.
95% a function of Tumblr tech listing, too - Stowe Boyd posted the exact same stats just a couple of days ago. Content is barely a factor at all - he’s produced 10x as many posts as my 480-odd, yet the follow-rate is bang the same.
ETA for 100,000 probably late March