Weekly Recap 053008: ‘No Comment’

The week that was…


Good discussion this week about comments…first, there was the latest installment of this issue: comment dispersion away from the originating blog…Fred Wilson at A VC weighed in: Jackson instigated the conversation with that post. His reward is the comments it generates…interestingly, bloggers with big established audiences agreed with him…Chris Brogan wrote this on Fred’s blog: One part of the currency I crave from doing a blog is that conversation, especially on my blog, where I spend lots of effort building the posts to be conversation starters, not just fully formed ideas…Mathew Ingram wrote a concurring blog post Bloggers get “paid” with comments

Which made me wonder, do you think there’s a divide between larger established bloggers and smaller, newer bloggers on this issue of distributed conversations?


Next up on the comment discussions…who actually owns the comments?…there was a controversy early in the week where Rob La Gesse was irritated at the comments that were occurring on FriendFeed about his blog post…so he pulled his blog RSS from FriendFeed, which eradicated that post and all its comments from the FriendFeed UI…this raised the question of who owns the comments, and whether FriendFeed should do a better job of keeping records…Mathew Ingram reached out to FriendFeed co-founder Paul Buchheit, who noted the bias is toward blogger control of their feeds and that they will look at ways to solve to better retain comments…

Later, Daniel Ha of Disqus wrote a post called A Commenter’s Rights…kind of a Bill of Rights for those who leave comments on blogs…one Right that I liked: ‘The ability to edit and remove their comments’…too many blogs don’t allow that, including wordpress.com…

We’ll close this out with a quote from my favorite cranky blogger Steven Hodson: This whole discussion about comments is becoming borderline stupid


FriendFeed is growing, and not surprisingly, it’s getting its share of…um…interesting personalities…click this link which takes you to a search for “tweets totally f%(#ed twitter”…you’ll understand what I mean…


Hats off to a couple of developers this week…I wrote a post titled FriendFeed ‘Likes’ Compatibility Index…I manually pulled together some stats to see which other FriendFeeders had the same Likes as me…well Yuvi wrote a script that he could run from his computer for any FriendFeed handle he entered…a bunch of us wanted our stats manually calculated, and he obliged…he blogged about it, and hit Techmeme…very nice Yuvi…

Then another developer, felix, created a UI where anyone could enter their FriendFeed handle to see the people who shared Likes the most…and then felix thought, “I’m going to turn this one up to 11″…he made pie charts out of the results, which have become a big hit on FriendFeed…FriendFeed co-founder Bret Taylor gave his thumbs up on felix’s blog, “Very cool!“…very nice work felix…

BTW, we’re all one playing for second place to Shey in the Likes department…


Jeremiah Owyang apparently has an interesting post on FriendFeed that he’s writing for Saturday 5/31/08…Robert Scoble talked with Jeremiah, and gave this update:

I just talked with Jeremiah. He says FriendFeed will turn on a new functionality that Jeremiah is calling “MiniMeme.” He wouldn’t give me more details, but I am intrigued.

So check your RSS reader for Jeremiah’s post, and maybe we’ll be talking about that here next week….


See this item on FriendFeed: http://friendfeed.com/search?q=%22Weekly+Recap+053008%3A+%E2%80%98No+Comment%E2%80%99%22&public=1

FriendFeed ‘Likes’ Index: Case Study in Value of Distributed Conversations

By keeping comments distributed, or decentralized, more than one discussion is able to take place. New ideas are likely to be heard since readers often start with a blank slate and are more likely to participate.

Shey Smith, introspective snapshots, The Case For Distributed Conversations

Today, a great example of the value of distributed conversations took place. What started as a blog post here ended up with three different developers coming up with innovative new scripts that FriendFeeders were digging. And it all happened because of distributed conversations, not despite them.

The very smart and in-tune Fred Wilson wrote a piece yesterday decrying the distribution of conversations all over the Web, including on FriendFeed. Mathew Ingram followed up with a concurring blog post. I understand where they’re coming from, but I think they overlook the value of distributed conversations.

What I’d like to do is briefly describe the action today, and then point out how distributed conversations made innovation possible today.

FriendFeed ‘Likes’ Index Calculators

Wednesday night, I posted a piece titled FriendFeed ‘Likes’ Compatibility Index. The post reported some number crunching I did to figure out who most often Liked the same things that I do. The idea was to see what other FriendFeeders shared the same interests. At the end of the post, I made a request for someone to automate the analysis.

From this post, two separate conversations emerged. The RSS feed for the blog post hit FriendFeed (Original Post). And Louis Gray shared it on Google Reader (Shared Post), which started a second conversation.

What happened? There were three different places where conversations were happening: on this blog and on two different items in FriendFeed. And it resulted in three separate developers coming up with solutions.


Yuvi, a 17-year old wunderkind who does amazing stats analysis, was interested in automating this analysis. He posted the same comment on all three locations: “I could automate this…if friendfeed fixed this bug.” Yuvi was concerned about a bug in FriendFeed that won’t allow you to go more than 11 pages back in your history.

His comment generated responses in FriendFeed on both the Original Post and on the Shared Post.

Original Post:

  • Phil Glockner: “Yuvi, does that bug exist when doing queries against the API?”
  • Yuvi: “Yes, it exists in the API too.”

Shared Post:

  • Shey: Yuvi, could you automate it up to page 11?
  • Hutch: Does the limit of going back beyond page 11 risk the script failing? Or does it limit the data collected?
  • Yuvi: @Hutch: Limits data collected.
  • Yuvi:@Shey: Well, I could… But, it’ll be of limited use, no?
  • Bwana: I say do it now so when they do fix it, it’ll be ready, plus there seems to be an interest
  • Shey: @Yuvi Limited yes, but I think 11 pages of data is of some use for analysis of recent data
  • Cyndy: Yuvi, I’m not sure it’s a bug. I think it’s a limit that they have set. Since the variable is passed in the URL, if you try to go past that number of posts manually, it still won’t go. Could be that they are only pulling from cache?
  • Yuvi: @Cyndy: Well, they’ve been mum on this – so I don’t really know. But, if even *I* can’t access my old stuff, isn’t that wrong on at least “some” level?
  • Benjamin Golub: I don’t think it’s a bug either. I feel that there DB sharding might be setup such that it is very very quick to pull recent data.
  • Bwana: Well if there is a limit imposed, pages after 11 shouldn’t even be shown. It’s a bug of some kind either way.
  • Yuvi: @Benjamin: Yep, agree on that, but there should be ‘some’ way to get the older data out, no?
  • Yuvi: Just repeating – the API has the same limit in place. Script ready anyway – First Target – LouisGray 😉

So in that sequence, you see that fragmented conversation, away from the blog post itself, resulted in Yuvi creating a script to determine who shares your Likes.

And Yuvi blogged about it, linking to my blog post and even mentioning me by name. Everything a blogger could want.

Do you see what I mean Fred and Mathew?

Ole Begemann

On Louis’s Shared Post, a second developer Ole Begemann weighed in:

  • Ole Begemann: I’ve written a Python script that does this, too (for practice). Interestingly, Phil is no. 12 on my list of Louis Gray’s most compatible likers. If there’s interest, I’ll try to wrap it up on a web page (it’s command line at the moment) and publish it.
  • Hutch: @Ole – Yeah, I’d like to have a page where you could see these results.
  • Ole Begemann: I’ll get around to it Hutch. It might take me a few days. It’s my first try as a Python programmer. 😉

A second developer came up with a script for this. Again, via conversations that happened entirely away from the originating blog.


Finally, back on my Original Post in FriendFeed, a developer named felix added this comment:

“I just created a little javascript to go and grab the last 30 likes of anyone and do a basic calculation. Have a couple more features I want to add, but no more time today – what do y’all think? http://is.gd/nLc

That link goes to a blog post, where Ole links back to my original blog post. Again, as a blogger who wrote something I thought might be interesting, this is all really good stuff.

None of it occurred on my blog. And it doesn’t bother me in the least! in fact, check out felix’s blog post. You’ll see that he, Yuvi and Ole are having a conversation about FriendFeed API limits.

Why the Distribution of the Conversation Made a Difference

Three points to make here.

1. Go where the conversations are

If I’d been hung up on forcing everyone back to my blog for comments, this likely would not have been as successful as it turned out. FriendFeed offers a dead simple commenting function that makes it incredibly easy to comment. People find it easy to interact around content, rather than everyone having to travel from blog to blog to hold conversations.

Some blogger removed his RSS from FriendFeed recently, because he didn’t like all the FriendFeed comments along with it. Really? I remember the story, but can’t find the link to his blog. Seriously.

2. Connect to people outside your blog subscriber base

Digg, StumbleUpon, FriendFeed…all of these give exposure to your blog outside of those who subscribe to it or bookmark it. And when conversations about your blog occur on these venues, you’re getting vital exposure.

Make no mistake about this. A Like or a Digg or a Stumble is great. But if you really want to attract people to your blog post, comments are king. They tell people that the post is interesting, and that they better go read to get in on the discussion.

Louis Gray has a bigger, and different, community than I do. So his share of the post on Google Reader, and the subsequent conversation, attracted people who might never have bothered with my post.

felix, who developed the really cool app where you can see who shares your Likes, does not subscribe to me in FriendFeed, nor does he subscribe to my blog. I looked at his subscriptions, and we do have a number of FriendFeeders in common including Louis. I presume that’s how he found his way to the conversation about the blog post. Would he have been attracted to the blog without the conversation going on inside FriendFeed? Unlikely.

Embrace distributed conversations. They are free advertising for your blog.

3. Use the everyone search feature

Have people figured out this one yet? On FriendFeed, you can run a search for your blog post title in the ‘everyone’ tab. It can be a little hectic, but also fascinating. Click here for the everyone search for the FriendFeed ‘Likes’ Compatibility Index post.

Note that not only will you see all the different instances of my original blog post. You’ll see Yuvi’s post as well as Thomas Hawk’s post on the subject. I like seeing comments on those related posts as well.

As a blogger, I get a lot of value out of seeing who liked the blog post, and all the conversations among the different tribes. They help me improve.

Final Thoughts

Would that blog post have resulted in three separate scripts being developed if conversations only happened on the blog? No. At least not for me. If you’ve got a huge subscriber base like Fred Wilson or Mathew Ingram, it might.

But if you’re small fry, the distribution of conversations provides enormous value. Now let me go see who shares my Likes on FriendFeed…


See this item on FriendFeed: http://friendfeed.com/search?q=%22FriendFeed+%E2%80%98Likes%E2%80%99+Index%3A+Case+Study+in+Value+of+Distributed+Conversations%22&public=1

FriendFeed ‘Likes’ Compatibility Index

A favorite feature of FriendFeed is the Like. You get to indicate your interest in an item with a simple click of the Like button.

The act of applying a Like does two things:

  • Provides feedback to the content creator
  • Reveals what your interests are

It’s that second point that is interesting. Amazon.com matches you to other shoppers based on what you buy in order to provide recommendations. Toluu matches you with others based on common RSS feeds. Diigo matches you based on common bookmarks and tags.

How about matching people based on common FriendFeed Likes? Call it the FriendFeed Likes Compatibility Index.

Curious about this, I went to my Likes tab on FriendFeed. I went back to my 50 most recent Likes, and tallied the number of Likes by others. By doing this, I figured I’d see with whom I had the most in common.

The top 29 people are shown below – I put the cutoff at having 4 Likes in common. Some of these folks I know, others I really haven’t interacted with yet.

Here are my top matches in FriendFeed:

  1. Atul Arora (13 likes in common)
  2. Louis Gray (13)
  3. Mitchell Tsai (11)
  4. Shey (11)
  5. Robert Scoble (10)
  6. Thomas Hawk (9)
  7. Julian Baldwin ( 8 )
  8. Jason Kaneshiro ( 8 )
  9. Mark Trapp (7)
  10. Charlie Anzman (6)
  11. Mark Dykeman (6)
  12. Bearded Dave (5)
  13. Bwana McCall (5)
  14. Mack D. Male (5)
  15. Mike Fruchter (5)
  16. Phil Glockner (5)
  17. Alejandro S. (4)
  18. Andrew Badera (4)
  19. Anthony Farrior (4)
  20. Dobromir Hadzhiev (4)
  21. edythe (4)
  22. Kenichi Matsumoto (4)
  23. Marco (4)
  24. Nikpay (4)
  25. Rob Diana (4)
  26. Ruth Ferguson (4)
  27. Shawn L Morrissey (4)
  28. Susan Beebe (4)
  29. Timothy Neilen (4)

One small observation – I’m not in sync with a lot of women, am I? What’s up there? FriendFeed Is from Mars, Twitter Is from Venus?

Now what I need to do is to subscribe to those on this list that I haven’t yet. Also of note – there were 241 different people with whom I shared a Like in this analysis. Really great how FriendFeed lets you come into contact with a wide range of people.

Would be cool if a script could automate the FriendFeed Likes Compatibility Index…


See this item on FriendFeed: http://friendfeed.com/search?q=%22FriendFeed+%27Likes%27+Compatibility+Index%22&public=1

Tag Recommendations for Content: Ready to Filter Noise?

In a recent post, I suggested that the semantic web might hold a solution for managing noise in social media. The semantic web can auto-generate tags for content, and these tags can be used to filter out subjects you don’t want to see.

As a follow-up, I wanted to see how four different services perform in terms of recommending tags for different content.

I’ve looked at the four services, each of which provide tag recommendations. Here they are, along with some information about how they approach their tag recommendations:

  • del.icio.us: Popular tags are what other people have tagged this page as, and recommended tags are a combination of tags you have already used and tags that other people have used.
  • Twine: Applies natural language processing and semantic indexing to just that data (via TechCrunch)
  • Diigo: We’ll automatically analyze the page content and recommend suitable tags for you
  • Faviki: Allows you to tag webpages you want to remember with Wikipedia terms.

Twine and Diigo take the initiaitve, and apply tags based on analyzing the content. del.icio.us and Faviki follow a crowdsourced approach, leveraging the previous tag work of members to provide recommendations.

Note that Faviki just opened its public beta. So it suffers from a lack of activity around content thus far. That will be noticed in the following analysis.

I ran the six articles through the four tagging services:

  1. The Guessing Game Has Begun on the Next iPhone – New York Times
  2. TiVo: The Gossip Girl of DVRs – Robert Seidman’s ‘TV by the Numbers’ blog
  3. Twitter! – TechCrunch
  4. Injury ‘bombshell’ hits Radcliffe – BBC Sport
  5. Why FriendFeed Is Disruptive: There’s Only 24 Hours in a Day – this blog
  6. Antioxidant Users Don’t Live Longer, Analysis Of Studies Concludes – Science Daily

The tag recommendations are below. Headline on the results? Recommendations appear to be a work in progress.

First, the New York Times iPhone article. Twine wins. Handily. At Diigo gave it a shot, but the nytimes tags really miss the mark. del.icio.us and Faviki weren’t even in the game.

Next, Robert Seidman’s post about Tivo. Twine comes up with several good tags. Diigo has something relevant. And again, del.icio.us and Faviki weren’t even in the game.

Now we get to the trick article, Michael Arrington’s no text blog entry Twitter! The table turn here. Twine comes up empty for the post. Based on the post’s presence on Techmeme and the 400+ comments on the blog post, a lot of people apparently bookmarked this post. This gives del.icio.us and Faviki something to work with, as seen below. And Diigo offers the single tag of…twitter!

Switching gears, this is a running-related article covering one of the top athletes in the world, Paula Radcliffe. Twine comes up the best here. Diigo manages “bombshell”…nice. del.icio.us and Faviki come up empty, presumably because no users bookmarked this article. And none of them could come up with tags of “running” or “marathon”.

I figured I’d run one of my own blog posts through this test. The post has been saved to del.icio.us a few times, so I figured there’d be something to work with there. Strangely, Twine comes up empty. Faviki…nuthin’.

Finally, I threw some science at the services. This article says that antioxidants don’t actually deliver what is promised. Twine comes up with a lot of tags, but misses the word “antioxidants”. Diigo only gets antioxidant. And someone must have bookmarked the article on del.icio.us, because it has a tag. Faviki…nada.


Twine clearly has the most advanced tag recommendation engine. It generates a bevy of tags. One thing I noticed between Twine and Diigo:

  • Twine most often draws tags from the content
  • Diigo more often draws tags from the title

Obviously my sample size isn’t statistically relevant, but I see that pattern in the above results.

The other thing to note is that these services do a really great job with auto-generating tags. For instance, the antioxidant article has 685 words. Both Twine and Diigo were able to come up with only what’s relevant out of all those words.

With del.icio.us and Faviki, if someone else hasn’t previously tagged the content, they don’t generate tags. Crowdsourced tagging – free form on del.icio.us, structured per Wikipedia on Faviki – still has a lot of value though. Nothing like human eyes assessing what an article is about. Faviki will get better with time and activity.

Note that both Twine and Diigo allow manually entered tags as well, getting the best of both auto-generated and human-generated.

When it comes to using tags as a way to filter noise in social media, both system- and human-generated tags will be needed.

  • System-generated tags ensures some level of tagging for most new content. This is important in an app like FriendFeed, where new content is constantly streaming in.
  • Human-generated tags pick up where the system leaves off. In the Paula Radcliffe example above, I’d expect people to use common sense tags like “running” and “marathon”.

The results of this simple test show the promise of tagging, and where the work lies ahead to create a robust semantic tagging system that could be used for noise control.


See this item on FriendFeed: http://friendfeed.com/search?q=%22Tag+Recommendations+for+Content%3A+Ready+to+Filter+Noise%3F%22&public=1

Why FriendFeed is Disruptive: There’s Only 24 Hours in a Day

Forget fractured conversations. How about fractured attention?

MG Siegler has a post up at ParisLemon titled FriendFeed Should Kill Those Who Accuse It of Murder. In the post, he writes that the current meme about FriendFeed killing Twitter and Google Reader is overblown and that all the services will exist in relative harmony for the foreseeable future.

To which I ask: did someone just extend the day to 25 hours?

Because there really is a zero sum game aspect to social media. We only have 24 hours in a day, and we have to decide where to spend those hours.

That daily time limit is what makes FriendFeed so disruptive.

Allocation of the 24 Hour Day

The chart below is a hypothetical day of a relatively advanced social media user (no laughs about Facebook please):

The chart shows our social media user at three different points. I’ve taken the liberty of assuming that certain core life stuff is maintained consistently: sleep, eat, work, family. All else is flex time.

So with the core life stuff constant at 19.5 hours, and more time spent on FriendFeed, something’s got to give? But what?

Not websites and blogs. In fact, their page views go up because of FriendFeed. Their content is the currency of FriendFeed conversations.

I think the two services that get hit the hardest as FriendFeed grows will be:

  • Twitter
  • Crowdsourced aggregators: Digg, Stumbleupon, LinkRiver, Reddit


I left this comment on Corvida’s post The #1 Reason FriendFeed Will Not “Dethrone” Twitter at SheGeeks.net:

My two cents. FriendFeed direct posts feel like Twitters, only you can see the whole conversation, not just part of it. FriendFeed lacks the @reply and DM, so if those are important use cases, yeah it’s not replacing Twitter. But for putting something out there and having your subscribers weigh in…well, it feels like Twitter.

I’m not the only one. Two heavyweights in the blogging world have expressed their feelings about using FriendFeed in lieu of Twitter:

  • Steve Rubel :”Who’s spending less time on Twitter and more time here? I am.”
  • Duncan Riley: “@geechee_girl true, and if I can switch to FF with everyone on Twitter, I’d start considering swapping most if not all of the time”

The key to Twitter’s success is not it’s haiku format, it’s the community, as Duncan Riley mentions. Twitter is growing fantastically, as more people adopt it (and unfortunately stress its current platform). That community is what makes it vibrant special. FriendFeed appears to be rapidly growing its own community. I’ll be curious what the Compete.com May numbers look like for FriendFeed.

Note in the allocation of the day, I don’t eliminate Twitter. People have built up their networks there, and tweeting has become a habit. Also, the @reply function is quite popular, as is the DM. One might ask if those functions aren’t essentially covered by instant messaging and email, but Twitter fans love ’em.

But I see the direct post + comments as taking interaction away from Twitter.

Crowdsourced Aggregators

The basic function of these applications is to surface the content receiving the most votes. Digg, StumbleUpon, Reddit and LinkRiver are great for discovering content that others have found valuable. Digg includes robust, active commenting.

Well, doesn’t that sound like FriendFeed? The system of ‘Likes’ and comments ensures that community-ranked content appears at the top of your FriendFeed page.

Again, FriendFeed doesn’t kill these services. StumbleUpon, for example, has a persistence to it that FriendFeed lacks. Content gets its moment in the sun on FriendFeed, then gets buried in pages further back. I’ve noticed the StumbleUpon activity around content can last for days, weeks.

But over time, as users discover ranked content on FriendFeed, I’d expect them to cut back their time on the other crowdsourced aggregators. Not stop using these other services, but check in on them less frequently.

Final Thoughts

Perhaps as MG Siegler said, there really is room for all of these social media apps. Folks will just expand the amount of time they devote to them. But I question that assumption. Your employer still pays you for your hours. Your kids still want your time. The human body needs its sleep. And you still need to eat.

FriendFeed is disruptive because it challenges a number of other applications. If you find something that offers an outstanding experience and provides a good percentage of what you like in other social media apps, wouldn’t you spend more time there?

I mean, there’s only but so many hours in a day.


See this item on FriendFeed: http://friendfeed.com/search?q=who%3Aeveryone++%22Why+FriendFeed+is+Disruptive%3A+There%E2%80%99s+Only+24+Hours+in+a+Day%22

Weekly Recap 052308: If You Love Your Blog, Set It Free

The week that was…


Things kicked off with a pair of posts about the next stage of blogging. Yes, fractured comments and all…Duncan Riley wrote Blogging 2.0: It’s All About The User. He writes: If blogging 1.0 was about enabling the conversation on each blog, blogging 2.0 is about enabling the conversation across many blogs and supporting sites and services…Louis Gray followed up with Blogging 2.0 Causing Friction With 1.0 Bloggers…Louis nicely defines the old blogging paradigm: Blogging 1.0 centered around who could: (i)Amass the most page views; (ii) Display the most ads; (iii) Get the most comments; and (iv) Attract the most RSS subscribers

As a relatively novice blogger, I pretty easily fall into the Blogging 2.0 camp…why on earth would I want to keep the conversations limited to my little blog?…that’d be a recipe for having a stale blog…

But Blogging 1.0 is still a strong instinct out there…one example: see Allen Stern’s post on CenterNetworks, Let’s Get Serious About FriendFeed; the 1995 Message Board, the Smart Consolidator and the Stolen Conversation…read not just the post, but check out some of the comments…Blogging 1.0 will die hard…


Help! I’ve fallen, and I can’t get up!…bad week for Twitter, everyone’s favorite social chat room: outages, outages, outages…this seems to be getting progressively worse, as Twitter’s success is killing it…

To show disapproval for Twitter’s handling of these outages, several folks staged a Twit-Out on Wednesday May 21…a number of regular Twitterers went the whole day without going over to Twitter…they also hid tweets from their FriendFeed streams…even the biggest Twitterer of all, Robert Scoble, joined in…

It wasn’t met with universal love, but they made their point…oh, and Twitter did go down that day…

But one bright spot: Twitter apparently scored a new $15 million round of VC funding…


One outcome of the twitter issues this week…some bigger names in the social media world started to embrace it much more…Jeremiah Owyang, who previously marked the date when new Twitter subscribers could not be considered as early adopters, got into it again with FriendFeed…first he posted on FriendFeed that he now had a new place (FriendFeed) to look for conversations, which elicited a bunch of hearty “welcome aboard” type of messages…

Well that got Jeremiah fired up, and went into throw-down mode: Dudes, I’ve been on FriendFeed for a while, not a late adopter…he challenged Robert Scoble to list his date of FriendFeed registration…geek cred…

Of course, if you looked at his activity stats at that time, he had no comments, no likes…but he’s much more engaged now, which is cool…he even wrote a post about FriendFeed…


One thing I’ve noticed in some favorited Flickr photos…models wearing little to nothing…not that I’m complaining, I love art…Thomas Hawk has some strong opinions about making this even easier here


FriendFeed now has Rooms!…Rooms are separate spaces on FriendFeed where people can direct post items, and re-share items into a Room…they accomplish two things: (i) allow a focus around specific topics to follow; (ii) remove some of the items that were considered noise by many users…

Bwana McCall (second reference in this post, nice!) has a good initial set of use cases for rooms here…my favorite is the use of Rooms for live blogging like from one of those Apple events…

One bit of hilarity was the land grab that occurred for Room topics…Michael Nielsen asked Any plans to prevent squatting? I can see people snapping up thousands of “rooms” on the off chance that one day they’ll be worth something…um, well, uh…I managed to score Web 2.0, Enterprise 2.0, Running, Obama 2008 and Coca Cola among others…no idea what I’ll do with them, but anyone’s free to join…I wonder if the Obama campaign will want their Room?

Something that Rooms will foster: an increase in FriendFeed direct posts…regular feeds from your social media sites won’t stream automatically into Rooms…


See this item on FriendFeed: http://friendfeed.com/search?q=%22weekly+recap+052308%22&public=1

Analyzing My FriendFeed Stats: I Should Be Direct Posting More

I’m curious about the level of interaction that occurs around the different content that streams through FriendFeed. Distributed conversations are fine by me, and I wonder what sparks them most often for content. So I did a little analysis of the ‘likes’ and comments that have happened for me.

Below are some pie charts. The first set analyze the ‘likes’. To the left is the percentage of my FriendFeed stream that comes from different content sources. To the right, I counted the number of ‘likes’ for the various content sources. For the ‘likes’ I only counted for the month of May, but I think it’s a decent approximation of my overall activity.

A couple observations:

  • Blog posts and FriendFeed Direct Posts are the biggest sources of ‘likes’
  • Google Reader shares and Twitter are a big part of my stream, but don’t generate a comparable percent of ‘likes’

Now let’s see how the comments look:

Would you look at that? FriendFeed direct posts dominate the comments. My blog posts are #2.

What’s It Mean?

I imagine everyone’s experience will vary. For me, I draw four conclusions.

My FriendFeed use is similar to people who Twitter: With FriendFeed direct posts, I’ll sometimes just make an observation. Other times, I direct post a website, generally with a graphic. This strikes me as similar to Twitter in that I’m posting something that can be consumed by anyone who subscribes to me. Also, these posts mean someone can stay within FriendFeed. Seems to make a difference in interaction when people can stay on the site. Like Twitter.

‘Likes’ dominate my blog posts: The Likes:Comments ratio for my blog posts is running at 4:1. For all the concern about fractured comments, I’d say people are overlooking basic recommendations of your content via ‘likes’. It’s not about the comments, it’s about the ‘likes’!

Comments on my posts frequently occur on someone else’s stream: There are several of my blog posts that have generated good comments. They just haven’t occurred on the RSS feed from my blog. These bigger comment fests have been when someone with much larger following and FriendFeed ‘presence’ (and I’m not going to write his name, because I use it too often…). But you know what? I’ll take those comments! They obviously weren’t happening just off my own post. In the long run that kind of exposure is vital for us smaller bloggers.

Google Reader shares suffer from repetition: Good blog posts will often be shared by several FriendFeed members, including those with larger followings. So when I share, I may be following others. So the repetition diminishes the interaction. I still share – there is some interaction. And Google Reader shares end up in several other places, like RSSmeme and ReadBurner. These services will show the most popular shares, so I want to vote for these blog posts.

Final Thoughts

Colin Walker has some interesting thoughts about using FriendFeed as a blogging platform. Looking at how FriendFeed Direct Posts and my blog generate the biggest activity, maybe he’s on to something.


See this item on FriendFeed: http://friendfeed.com/search?q=%22analyzing+my+friendfeed+stats%22&public=1