Curating my streams on Flipboard
The killer app
A few weeks ago, Flipboard (often referenced to as the iPad killer app”) was finally launched for the iPhone. And what the developers did to transport the flowing way of navigating your streams and news sources to the much smaller screen of the iPhone is more than convincing. Flipboard has very quickly become my favorite app for reading Twitter, Facebook or Instagram.
But there is one thing that really shows the future of curating news: With Flipboard everybody can generate his own stream of news-sources and make it available for other users. Actually, I believe Jon Russell, is wrong. He complained earlier this day, that
“the news is somewhat US/Western-centric for me. While it is true to say that, particularly when talking tech (though I am referring all news genres), the lion’s share of media is US based. But, if I want personalised news, and the chance to discover content, should there be more emphasis on varied sources?”
Two flavors of personalization
There are two different varieties of personalization. The “old” variety that surfaced with the first dynamic web pages based on CGI scripts, allowed the user to select from a number of options defined by the editor (or webmaster). This variety is based on the notion of more or less homogeneous audiences or large groups of people. In this world you could chose between European, North-American or Asian news sources the same way you could chose between Tech, Culture or Politics channels. But you could not create, edit or share these streams.
The “new” variety of personalization allows to do exactly that. You can not only chose your streams but create, edit and share your streams. In Flipboard the most convenient way to do this is via Twitter lists. You only have to create a Twitter list of people or sources (= bots) that reflect your information needs the best, add this list to your Flipboard and off you go with your personalized news ecosystem in the “new” variety. You can also share your news list by making the list public. This way you can not only curate content, but also curate interesting people.
Writing for Flipboard
Another option is to curate your own Flipboard channel via a Twitter account, Facebook account or a generic RSS source such as a blog. I believe that there will be many applications resembling Flipboard in the future, so that sooner or later it will make sense for media companies to have a “Flipboard curator” that knows how to write optimized for this kind of aggregators.
It is no big surprise that Obama’s inauguration generated a lot of buzz on Twitter. The hashtag #inaug09 quickly became one of the most frequently typed Twitter tag. But exactly how often did people tweet about Obama’s inauguration? We have quite detailed data about this day on Facebook:
Since this morning more than 1.5 million status updates have been posted through the feed (there were 200,000 b 8:30 am PST). During the broadcast an average of 4,000 status updates were written every minute, and 8,500 were written every minute during Obama’s speech.
Twitter only published a chart on the corporate blog – a chart without numbers:
We saw 5x normal tweets-per-second and about 4x tweets-per-minute as this chart illustrates.
But how much is 5x normal or 4x normal? Mashable wrote: “Tweets containing “Obama” hit 35,000 per hour during his speech.” This would be 583 Obama tweets per minute. Not too much.
My method: Fortunately every tweet has its own id which is simply increased by 1 with every new tweet. So to calculate the average Twitter speed during Obama’s inauguration, I looked for the first Obama tweet that had been posted at 12:00 and the first tweet at 1:00. The difference between those two is the number of tweets published during this hour.
The result: During 12pm and 1am, there had been 4746 tweets per minute (TPM).
One of the new features of TwitterFriends is a visualization of your incoming and outgoing networks. Remember: Your incoming network includes all Twitter users that replied or referred to you on Twitter the last 30 days at least twice. And the outgoing network – your relevant net – includes all Twitter users you referred or replied at least twice. You can see this new diagram on your stats page:
http://twitter-friends.com/?user=kevinrose. Just replace kevinrose with your Twitter username. No password required.
Kevin Rose’s diagram (I chose him because he’s the Twitter user having the largest incoming network) looks like this:
You can see three things on this diagram:
- Popularity: The network of people addressing him is much larger than the network of people he’s addressing. It’s almost eight times larger (see number in the left box of the statistics page). This measure goes in the same direction as the Friend-to-Follower-Ratio. If a lot of people are sending you replies and you are only able to reply to a small fraction of them, you’re popular. So, the different sizes of the two networks are a measure for popularity.
- Overlap: The overlap is not too large. In fact it’s only 28.6% (see number in the left box of the statistics page). There are many people trying to talk to Kevin Rose on Twitter that are not receiving replies. But on the other hands there are Twitter users, Kevin Rose talks to that do not answer his replies regularly or their accounts are protected – TwitterFriends does not analyze protected data, even if you log in with your Twitter credentials at the top of the page.
- Friends: If you want to know more about the people in the overlapping area, take a look at the full user cloud either incoming or outgoing.
Those names with a bidirectional arrow are contacts that are talking to Kevin Rose on a more or less regular basis and receiving Twitter messages by Kevin Rose as well. Of course, these users are not always what we’d are calling “friends” in real life, but they are regular conversation partners.
Now you can take a look at your own TwitterFriends statistics and compare your Venn diagram with the above. Are your networks more overlapping? Do you have a larger incoming than outgoing conversational network as well? If you experience a bug or have a great idea about how to improve TwitterFriends, send me a Twitter message to @furukama or just submit your input to the TwitterFriends support forum.
Valdis Krebs just wrote a great blog post about his strategy of maximizing his Twitter network efficiency. We all know, the more people you are following on Twitter, the more difficult it gets to keep in touch with all of them. Most often you are reducing your network size by selectively reading about and replying to the people you really care about (the relevant net) or the people you are talking to you (reciprocity).
But if you are looking at your Twitter network as a informational network (and not so much as a relationship network), it is more important how your contacts are related. If you are using your network to keep informed about many different topics, it pays to build a heterogenous network of many people from wholly different areas of expertise. Valdis put it this way:
The trick is to find the people that reach many social circles and follow them. Of course, we need to find more than the minimum of people to follow — you want some redundancy in your network so that there are multiple paths to places of interest for you.
In Social Network Analysis (SNA), there are some ways to put this notion into quantitative metrics. Ron Burt described a measure of redundancy in his greatly acclaimed work on Structural Holes. Stephen Borgatti continued this line of thought and developed a set of metrics to describe redundancy, density and network efficiency. This last measure interested me because it provides a way to measure the effective size of your network – how it would look like without redundant nodes. I’ve written a short function for TwitterFriends to display this measure on the network tab:
Twitter Network Efficiency for @furukama
After looking at some other Twitter users’ networks, @valdiskrebs’ network efficiency of 95.01% still ranks among the highest values. So, his strategy of building a diverse network that allows for information from different sources to reach him, seems to be quite successful.
To find out your Twitter Network Efficiency, visit this site: http://twitter-friends.com/?user=furukama&mode=net and replace the username with your Twitter username. Maybe the value will seem high at the beginning, but this can be because not all your friends’ connections are in the TwitterFriends database yet. Click on the names below the graphic to load them.
Now, I’ve finally rolled out the network visualization mode for TwitterFriends. It does not show the entire network of Twitter contacts (followees and followers) because it would simply be too large and confusing. Also, it would be not very meaningful, since contacts are easier added than removed. Therefore, only the “relevant network” of contacts, a person responded or addressed at least twice with the ‘@’ syntax will be visualized. This is the hidden network of people, a person is giving his best attention publicly on Twitter. After entering a Twitter user name, the ego network will be drawn. Here the one for my Twitter account @furukama:
@furukama's TwitterFriends network
The size of the nodes of the network corresponds to the number of Tweets, I have written to my Twitter contacts. Thus they represent the intensity of communication between the central or ego node and the relevant contacts in the network. As this visualization can become very crowded for users with many contacts, you can surf through this network: By clicking on a node, the network centers around it, allowing you a deeper look into the connections between the nodes in your relevant network:
Looking at @saschalobo's network in @furukama's network
Below the network graphic, there is a link for toggling between the simple network representation above and full FOAF visualization (FOAF for “friend of a friend“). The second visualization also includes the contacts of my contacts, which are not in my network – my “friends’ friends”. This increases the number of nodes once again, but you can click on nodes to navigate the network. Here’s the full network for Robert Scoble:
@scobleizer's FOAF network
And here’s mine:
@furukama's FOAF network
Note: For some users the network may initially appear quite empty because the network data of their contacts are not yet cached. In this case, there will be a list of links below the network graph. By clicking on them you can collect the missing data. Users with private profiles or who have not used @replies can not be included. You do not need to enter your Twitter credentials to visualize your network.
Comments and bug reports via Twitter to @furukama or in the comments. Thank you!
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