unwrapping
unwrapping:

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)!
Learn more about Union Metrics reblog trees.

unwrapping:

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)!

Learn more about Union Metrics reblog trees.

futurejournalismproject
futurejournalismproject:

What Happens When You Like Everything?
Journalists can be a masochistic lot.
Take Mat Honan over at Wired who decided to like everything in his Facebook News Feed:

Or at least I did, for 48 hours. Literally everything Facebook sent my way, I liked — even if I hated it. I decided to embark on a campaign of conscious liking, to see how it would affect what Facebook showed me…
…Relateds quickly became a problem, because as soon as you like one, Facebook replaces it with another. So as soon as I liked the four relateds below a story, it immediately gave me four more. And then four more. And then four more. And then four more. I quickly realized I’d be stuck in a related loop for eternity if I kept this up. So I settled on a new rule: I would like the first four relateds Facebook shows me, but no more.

So how did Facebook’s algorithm respond?

My News Feed took on an entirely new character in a surprisingly short amount of time. After checking in and liking a bunch of stuff over the course of an hour, there were no human beings in my feed anymore. It became about brands and messaging, rather than humans with messages…
…While I expected that what I saw might change, what I never expected was the impact my behavior would have on my friends’ feeds. I kept thinking Facebook would rate-limit me, but instead it grew increasingly ravenous. My feed become a cavalcade of brands and politics and as I interacted with them, Facebook dutifully reported this to all my friends and followers.

After 48 hours he gives up “because it was just too awful.”
Over at The Atlantic, Caleb Garling plays with Facebook’s algorithm as well. Instead of liking though, he tries to hack the system to see what he needs to do so that friends and followers see what he posts:

Part of the impetus was that Facebook had frustrated me. That morning I’d posted a story I’d written about the hunt for electric bacteria that might someday power remote sensors. After a few hours, the story had garnered just one like. I surmised that Facebook had decided that, for whatever reason, what I’d submitted to the blue ether wasn’t what people wanted, and kept it hidden.
A little grumpy at the idea, I wanted to see if I could trick Facebook into believing I’d had one of those big life updates that always hang out at the top of the feed. People tend to word those things roughly the same way and Facebook does smart things with pattern matching and sentiment analysis. Let’s see if I can fabricate some social love.
I posted: “Hey everyone, big news!! I’ve accepted a position trying to make Facebook believe this is an important post about my life! I’m so excited to begin this small experiment into how the Facebook algorithms processes language and really appreciate all of your support!”

And the likes poured in: “After 90 minutes, the post had 57 likes and 25 commenters.”
So can you game the Facebook algorithm? Not really, thinks Garling. Not while the code remains invisible.
At best, he writes, we might be able to intuit a “feeble correlation.”
Which might be something to like.

futurejournalismproject:

What Happens When You Like Everything?

Journalists can be a masochistic lot.

Take Mat Honan over at Wired who decided to like everything in his Facebook News Feed:

Or at least I did, for 48 hours. Literally everything Facebook sent my way, I liked — even if I hated it. I decided to embark on a campaign of conscious liking, to see how it would affect what Facebook showed me…

…Relateds quickly became a problem, because as soon as you like one, Facebook replaces it with another. So as soon as I liked the four relateds below a story, it immediately gave me four more. And then four more. And then four more. And then four more. I quickly realized I’d be stuck in a related loop for eternity if I kept this up. So I settled on a new rule: I would like the first four relateds Facebook shows me, but no more.

So how did Facebook’s algorithm respond?

My News Feed took on an entirely new character in a surprisingly short amount of time. After checking in and liking a bunch of stuff over the course of an hour, there were no human beings in my feed anymore. It became about brands and messaging, rather than humans with messages…

…While I expected that what I saw might change, what I never expected was the impact my behavior would have on my friends’ feeds. I kept thinking Facebook would rate-limit me, but instead it grew increasingly ravenous. My feed become a cavalcade of brands and politics and as I interacted with them, Facebook dutifully reported this to all my friends and followers.

After 48 hours he gives up “because it was just too awful.”

Over at The Atlantic, Caleb Garling plays with Facebook’s algorithm as well. Instead of liking though, he tries to hack the system to see what he needs to do so that friends and followers see what he posts:

Part of the impetus was that Facebook had frustrated me. That morning I’d posted a story I’d written about the hunt for electric bacteria that might someday power remote sensors. After a few hours, the story had garnered just one like. I surmised that Facebook had decided that, for whatever reason, what I’d submitted to the blue ether wasn’t what people wanted, and kept it hidden.

A little grumpy at the idea, I wanted to see if I could trick Facebook into believing I’d had one of those big life updates that always hang out at the top of the feed. People tend to word those things roughly the same way and Facebook does smart things with pattern matching and sentiment analysis. Let’s see if I can fabricate some social love.

I posted: “Hey everyone, big news!! I’ve accepted a position trying to make Facebook believe this is an important post about my life! I’m so excited to begin this small experiment into how the Facebook algorithms processes language and really appreciate all of your support!”

And the likes poured in: “After 90 minutes, the post had 57 likes and 25 commenters.”

So can you game the Facebook algorithm? Not really, thinks Garling. Not while the code remains invisible.

At best, he writes, we might be able to intuit a “feeble correlation.”

Which might be something to like.

Facebook bans Like gating

Like gating, e.g. ‘like our page to enter this competition’, will be banned after November 5th this year.
The announcement made on Facebook’s developer blog, explains that Facebook are updating their Platform Policy

To ensure quality connections and help businesses reach the people who matter to them, we want people to like Pages because they want to connect and hear from the business, not because of artificial incentives.

Is this a sincere move to ensure meaningful connections on Facebook? Or is it a shrewd business move to encourage pages seeking new ‘likes’ to spend more on Facebook advertising?

unwrapping
unwrapping:

Age Ranges of Tumblr’s Global Audience:Tumblr sees about 150 million global unique visitors monthly. comScore, an Internet analytics firm, averaged Tumblr’s age ranges over the first quarter of 2014 for both Dashboard and blog network traffic worldwide:
Ages 13 to 17: 15%
Ages 18 to 34: 41%
Ages 35 to 54: 29%
Ages 55 and up: 15%
"People are often really surprised to note that we have the same percentage of 55-plus-year-olds as we do 13-to-17-year-olds," said Danielle Strle (strle), Tumblr’s director of product for community and content, in an NPR webinar. “But over half of our audience is solidly in the 13-to-34 demographic.”

unwrapping:

Age Ranges of Tumblr’s Global Audience:
Tumblr sees about 150 million global unique visitors monthly. comScore, an Internet analytics firm, averaged Tumblr’s age ranges over the first quarter of 2014 for both Dashboard and blog network traffic worldwide:

  • Ages 13 to 17: 15%
  • Ages 18 to 34: 41%
  • Ages 35 to 54: 29%
  • Ages 55 and up: 15%

"People are often really surprised to note that we have the same percentage of 55-plus-year-olds as we do 13-to-17-year-olds," said Danielle Strle (strle), Tumblr’s director of product for community and content, in an NPR webinar. “But over half of our audience is solidly in the 13-to-34 demographic.”

analyticisms
analyticisms:

Great stats about the impact of social media on e-commerce and retail purachases:
Social media accounts for roughly half of all online and in-store purchases.
Four in ten social media users have purchased an item online or in-store after favoriting or sharing on Facebook, Twitter or Pinterest.
Half of those purchases took place within one week of sharing or favoriting.
68 percent of Facebook users are “lurkers” who rarely post, which means social media analytics cannot measure the influence of social on lurkers’ purchasing decisions.
Pinterest drives spontaneous purchasing more than any other network.
Social media-related purchases that come from Twitter and Facebook are made by users who are already interested in a particular product.
Facebook is the network most likely to generate purchases; nearly one in three users have purchased an item after liking, sharing or commenting on it.

analyticisms:

Great stats about the impact of social media on e-commerce and retail purachases:

  • Social media accounts for roughly half of all online and in-store purchases.
  • Four in ten social media users have purchased an item online or in-store after favoriting or sharing on Facebook, Twitter or Pinterest.
  • Half of those purchases took place within one week of sharing or favoriting.
  • 68 percent of Facebook users are “lurkers” who rarely post, which means social media analytics cannot measure the influence of social on lurkers’ purchasing decisions.
  • Pinterest drives spontaneous purchasing more than any other network.
  • Social media-related purchases that come from Twitter and Facebook are made by users who are already interested in a particular product.
  • Facebook is the network most likely to generate purchases; nearly one in three users have purchased an item after liking, sharing or commenting on it.