Social Media Research Blog

FriendFeed Vintage Posts

So last month I had run a bunch of FriendFeed crawls. Due to various other higher priority tasks, I haven't yet worked on having the crawl run 24X7. Still the data I have continues to bring forth interesting nuggets. In this post I would like to share the distribution of the friendfeed posts per year.

Year    Number of Posts
2008    439949
2007    35920
2006    7996
2005    1782
2004    147
2003    3
2002    2
2001    1

I had fetched the most recent updated entries for each user. Upon examining the entry dates, I found that some posts dated back to even before friendfeed existed! This was basically due to the fact that FriendFeed is just an aggregator and when a user adds her del.icio.us feed old posts from del.icio.us, amazon and other sources are integrated into FriendFeed. Ofcourse, pretty much all of the recent entries for more prolific users are dated 2008. In case you are wondering, here are the links to the oldest 5 entries. (There were a few entries dated 1989 -- but I think maybe those might be date errors).

1. March 26, 2001 del.icio.us entry by Wilfred 
2. May 10, 2002 Amazon wishlist by Amber
3. Nov 3, 2002 Amazon wishlist by Jumagun
4. Dec 8, 2003 Stumbleupon entry by Michael
5. Dec 12, 2003 del.icio.us entry by Mihir

This is but a snapshot of FriendFeed data and is perhaps only representative of a few days worth of crawls. But what it shows is that at any point in time there are still some "vintage posts" in the FriendFeed cellar for us to enjoy and share.

Maybe there is some joy in reminiscing over old entries with your friends. Perhaps it is sort of the same feeling that one has when sitting down with an old shoebox full of pictures and sharing it with your family. Or... maybe those posts reminds us of a time few years back when we were all excited about del.icio.us, blogs and wikis -- or whatever else was the latest, coolest next big thing at that time!

So my question for you is -- when was the last time you looked for the oldest piece of post/entry you (or your friend) shared online and what do you think about it now? Is it funny now? perhaps even embarassing? :-)

Wikimatix: Wikify and Disqus Hot Search Keywords

Introducing WIkimatix.com: A mashup for wikifying Google Trend's top search keywords. Moreover you can "disqus" and comment on recent hot topics being searched for right now!

Yesterday, while taking a break from writing my dissertation I was looking at the top search terms over at Google Trends. Few things immediately came to my mind:

1. I am a Wikipedia and News junkie -- no surprise there! Sometimes, I do not know a whole lot about the top search trerms and have to look up the wikipedia entry to refresh my mind about the topic or person. BTW, have you noticed that so many of the top terms are named entities? I also spoke about this in my earlier post on Intrestingness and Feshness in Search.
2. Google trends does not have an ability to discuss. I wanted something where I could comment on the terms in a digg like fashion? Perhaps talk to everyone about what I think of this whole "Big foot thing in the news". (Tip of the hat to the awesome widgets powered by Disqus)
3.  Finally, I would like to know exactly why something is in the News or has made it to a top 100 list. I just want a one or two line snippet/summary and a pointer to the most authoritative blog post/news piece about this keyword. While Wikimatix currently does not provide this -- it is something that can be easily extended.

Well, this was a quick hack ( ~150-200 lines of code; ~2 hours) and is meant to be a prototype/proof of concept idea. I think there can be a lot one can do with the idea of capturing the buzz using query logs and having discussions around the search terms and the latest news.

Note: Wikimatix updates itself every two hours. The disqus pages are associated with the individual search term and thus can be accessed even after the term is no longer buzzy.

Some possible improvements that might be good:

• Group related search terms like "big foot", "big foot discovery" etc
• Name disambiguation can be a problem. Sometimes the right person's Wikipedia entry is hard to determine without the context of the story.
• Simple heuristics might work rather well to identify the top categories that are in the news. For instance, today it is all about "Big foot", "Olympics" and a few other major events.
• For search terms, there might be a good video, photograph associated with it. It would be cool to display that as well.

Information Filters and Subscription Predictability

I read a superb blog post "Who is your Information Filter?". I think Hutch makes an important point here (text in bold is highlighted by me):

In an equal world, information shared by any of your friends will merit click-throughs and discussion. But the practical reality is that some people will be more “equal” than others in terms of driving the discussion agenda. There are two highly correlated components to that:

• Number of subscribers
• Reputation for identifying what is interesting

The sheer number of subscribers make some people Information Filters. The big power users on Twitter: Leo Laporte, Dave Winer, Robert Scoble, Jason Calacanis, etc. These guys really drive discussions around ideas, opinions and news. If you subscribe, you can’t help but be overwhelmed by the discussions they can kick off.

Subscribing to somebody's feed is like a commitment. On social network sites, your actions often speak for themselves. The posts one comments on or likes are quite predictive of who you subscribe to. This is what I found from the analysis of the "likes" in FriendFeed. The following graph shows the probability that a user A subscribes to a user B, given that User A "likes" k or more posts by user B. Typically, if a user has 'liked; more than 3 posts of another users there is over 80% probability of a subscription.

On one hand, this is because users are more exposed to posts from those whom they subscribe to. On the other hand, as the number of such actions between two users increases, so does the probability that there will be a continued commitment in terms of a subscription. Similar trends are observable from the "comments" graph.

The way I see it, an important implication of this is ensuring that the social network makes it possible for users to find and "try out" feeds they might "like". Findability and explorability of social networks allows users to initially explore posts from others and then decide which of these users they would like to pick as their "Information Filters".

Locality of Reference in FriendFeed Interactions

While I was having a discussion about my previous FriendFeed posts, I had mentioned that it would be interesting to see the split up of likes w.r.t the directed, shortest path distance. Following graph shows the number of likes that are received from people you directly subscribe to, or subscribe to 2 hops away, 3 hops away and so on...

Both from this graph and the earlier post, we can see that almost 45% of the likes are from people you directly subscribe to. But what is really interesting is that almost 90% of the remaining likes are accounted by friends who are just two or three steps away. FriendFeeders sure are a close bunch!

This type of locality of reference is also related to what sociologists have called the triangle closing. Partly, this is also due to the fact that FriendFeed tries to make it easier to attain "closing of triangles" by displaying the updates from friends who are just one step away. It would be worth looking into how this network's evolution takes place by recrawling FriendFeed subscription graph and observing how it changes. I suspect that through the likes and comments users find new friends and subscribe to their feeds: Your Friend's Friends who like you may be quite likable themselves!

I believe that models of Web graphs (small world, preferential attachment) are getting even more sophisticated in such social settings. The act of linking is not just creating a new hyperlink anymore -- but it is fragmented into actions such as "I subscribe to someone", "I comment on someones posts", "I like someones post". Such actions are not limited to FriendFeed alone. You can draw similar analogies in Youtube (watching, rating, commenting...), Flickr etc. As we explore these datasets through our empirical analysis, it might bring in a better understanding and intuition to build newer models for Social Graphs.

This data was generated using the 200,000 likes and the graph consisting of subscription information of over 75,000 users. The actual shortest path calculation was done using the Boost Graph Libraries (BGL). This is an amazing and extensive tool for graph analysis and quite frankly, I am a bit surprised that I hadn't used it before! I had used the MATLAB bridge but for very large graphs it is best to run the algorithms using BGL. But, more on that later!

Others Like You Too

Continuing with the FriendFeed comment and likes analysis. Here is a graph showing the percentage of self comments vs. comments from those you subscribe to vs. comments from others. This is based on roughly 700,000 comments and 200,000 Likes

Again, as noted by others the number of self comments are skewed by the fact that notes in del.icio.us and stumbleupon etc are treated by FriendFeed as individual comments. I have not compensated the data for this effect.

It gets interesting when we look at the Likes. I would have expected that the most likes would come from your friends. But likes from people you do not subscribe to slightly outnumber the likes from your friends. See.. others like you too!

That is the beauty of FriendFeed: discovering interesting memes from random people. Ofcourse it would be even more cool to see how many hops away most these people are. I suspect that they would actually be kind of close in the social graph.

Services That Attract Most Likes on FriendFeed

Over at FriendFeed, I have been having a superb conversation about my previous posts. John Duff asked me if I could run a similar analysis on the "Likes". Here it is, the most liked services on FriendFeed. Yep! FriendFeed is the most loved  raking in 50% of the likes. Closely followed by Twitter and Blogs.

Sharing good things is good. And FriendFeed makes it downright simple.

Services That Attract Most Comments on FriendFeed

Indexing about 400,000 posts and over 650,000 comments from about 70,000 users on FriendFeed gives some interesting data. There are about 45 different services that FriendFeed connects to at this point of time. First, I wanted to see what were the absolute volume of posts were from each of these services:

Next, I pulled up the number of comments that every service draws during the time period of my index (which was built by fetching the most recent posts and comments from the API).

Note that this is not normalized for multiple updates by the same user. A few interesting points:

• Large volumes of updates are from URL sharing and bookmarking tools like del.icio.us, Google Reader and StumbleUpon
• Most Comments are attracted by posts that were made directly via FriendFeed.
• del.icio.us does attract an equally large volume of comments.
• While Twitter is not far behind and receives a significant share.

Finally, for the services that attract the most comments, I have calculated the average number of comments per post:

The above chart says something important. On an average, original content like what one would find on blogs, still attracts the most comments. Followed by conversation focussed services like FriendFeed and Twitter. Finally, images posted on Flickr get a lot of attention too. Bookmark sharing sites are now lower in the ladder. See some trend here?

The attention and appeal one receives in social networks is a function of the original content you produce and the value, insights you bring to the table when you share new stuff.

Ranking FriendFeed Users

I read this interesting approach being used to rank FriendFeed users. The ranking is based on Google's search results page and is therefore a mix of FriendFeed linking and webpage links. I had been running a small crawl of FriendFeed users for the past couple of days. I started at Bret Taylor, one of the founders and followed all the users he subscribes to and went from there to discover more users, based only on the subscriptions. The crawl hasnt finished yet and there are roughly 22,000 user's subscriptions that I have fetched (so far).  Nevertheless, the graph that I found so far can be used to compute the PageRank of users based on who subscribes to whom. Following are the top 10 users based on PageRank from the subscription graph.

1. Thomas Hawk
2. Stefan Hayden
3. Duncan Riley
4. Robert Scoble
5. Jason Calacanis
6. Andrew Baron
7. Mrsth
8. Julio Medina
9. Marco
10. Czar D.J. Peterman

Take these results with a grain of salt. Firstly, because the crawl is incomplete and secondly because I think that rather than subscriptions the comment network says more interesting things. That said, what really surprised me about the top users is that I would have expected it to be dominated by the regular Blogosphere rockstars (aka A-Listers), but there are new faces up there -- more evidence that A-List is dead! People care more about interesting stuff and not the celebrity status of a handful and FriendFeed is a perfect place for finding and surfacing new stuff.

I am collecting "comments" and "likes" statistics from the crawl. We can similarly compute the PageRank over the comments graph. This is a fantastic dataset and has some wonderful properties, rating (ie likes) and temporal data.

As and when the crawl finishes, I will try to crunch it and post a few interesting nuggets.

What is the Dunbar's Number for Social Networks?

Many folks are really excited about FriendFeed. Personally, I have found that there are a lot more comments when something gets posted on FriendFeed. Recently Yuval Atzmon's User21 blog released a list of most followed users on FriendFeed. Since I too had a crawl of FriendFeed running in much the same way as Yuval, I decided to look at the complementary question: "How many users do people follow on FriendFeed"? While the crawl is not yet complete (and complete statistics would have to wait), the numbers are really striking! Some users follow more than a 1000 "friends":

sthayden	3190
scobleizer	3087
juliomedina	2760
thomashawk	2557
jasoncalacanis	2447
theillife	2045
mrsth	1961
pookakoo	1814
czarphanguye	1736
brynyoungblut	1716
eposter	1562
susangrisantiguitarist	1550

I find this really amazing. Unlike Twitter, FriendFeed posts are accompanied with longer conversations so it can be more involved. I can barely keep up with all the information flying past me everywhere right now! I guess, 1500+ "friends" would be way too much for me!

Sociologists often talk about the Dunbar's Number which

is the supposed cognitive limit to the number of individuals with whom any one person can maintain stable social relationships.

In human contact network the Dunbar's number is said to be around 150. It might as well be the case that social tools and especially, microblogging is pushing this limit further. Studies on Twitter, Livejournal and other social networking sites seem to support this observation. I wonder then: what would be the Dunbar's number on social networks? 300? 500??? Any guesses? Perhaps some comparison across all the published papers that have studied different social networks might have some clues.

[BTW, I am akshayjava on FriendFeed]

Watching your Work

Via Marc Pickett I

Here is a cool script that Marc wrote to help improve productivity. It takes a screenshot every minute, so you can view a "timelapse" of your day (or week even). See how much time you spend on wikipedia, email, video games, and writing time management scripts vs. doing actual work.

The video is 6.8 hours crammed into 17 seconds. Marc says he was on the train from Cambridge, so he wasted less time on the internet than normal. So the apparent productivity was'nt perhaps cuz big bother was watching, eh?

But this is a really neat way to summarize how you spent your time during the day. Thanks Marc, for sharing the script and the video with us!

Download Watchme script