Social Media Research Blog

Bacon + Biscuits =?

If you were still wondering what this whole internet obsession with bacon was all about -- here is the explanation of the "Bacon Mania" over at Salon (via John Scalzi's blog):

"Blogger Sadie Fox, who also goes by the name Miss Cellania, wrote about the online bacon bonanza in a post for Mental Floss last summer. She dates the burgeoning phenomenon back to September 2006, when blogger John Scalzi momentarily captivated the blogosphere by taping bacon to his cat. If there is a better example of the sublime pointlessness of Internet memes, I cannot think of one. Oh, wait: Yes I can."

(rdfs:seeAlso Twitter, Flickr and on the Web -- almost a certain way to get comments!)

Well now its all about the biscuit. Over at friendfeed people are changing their names and posting nutty biscuitty comments. It is being described as a "Mini Meme", although I am not sure how this one started. Marketing and advertising companies would pay a ton for starting such memes. Apparently, by the end of 2007, Bacon sales were significantly up in UK -- hmm, wonder if it had anything to do with the meme? ;-) Well, that is how the cookie crumbles! oh wait -- the biscuit crumbles.

Care for a Bacon Biscuit anyone?

Oh well.... nevermind!

[Image credits: http://www.recipezaar.com]

Share the Love

What makes social media so compelling is the ability to share information with others. ShareThis is a cool widget I use on my blog to enable users to share the posts with others and to easily post it on different sites like digg, StubleUpon, FriendFeed, etc or via email. Recently ShareThis commissioned a study conducted by Forrester Research, that analyzed the sharing patterns across a number of users. Following is an excerpt from their blog post:

High level findings:

• Email is still primary channel for sharing -- 69 percent of adults cite email as the primary source of receiving information
• Less than one-third of online adults said they learned about the new content from generally shared content sources such as YouTube, a wiki, or social networking sites
• 64 percent of adults and 60 percent of youth still use the traditional cut and paste  method to share a URL or information
  • However this is not trackable by publishers, making sharing by email tough to measure
• Though the primary motivation for sharing differs, 81 percent of adults claim that, when cutting and pasting content to share, they share to help others - believing that a person will benefit from the information they share

Surprisingly, email is still the primary channel for sharing and what was more interesting is that people still do it the old fashioned cut-and-paste way. The neat thing about using a tool like ShareThis is that it gives you a "one-click" way to share something with your network via email, IM or on any social media site. I somehow think that email is not quite the right format for sharing links and interesting articles. I like email to be more about actionable items: but then that is just me. I think FriendFeed, Twitter or Facebook is more suited for sharing links and other cool stuff.

There are a number of interesting nuggets on sharing demographics and differences in sharing behavior based on gender. From the post:

• The motivation for sharing amongst men differs than those of women
  • In general, men are more likely to share something to appear cool or to look like an expert
  • Men more likely to share product recommendations and video than women; 77 percent of adult males and 74 of younger males shared news and web links
• Women are more likely to share products or ideas they like via easy or direct sharing methods
  • Women strongly favor send to a friend feature; more than 60 percent of adult woman use the send to a friend feature on websites
  • Women are more likely to share directly, especially via cell phone texting
  • Adult women tend to face more obstacles when trying to share content online, with 40 percent saying they could find no ways to share the information directly from the Web page

I read through parts of the report I had received from ShareThis -- lots of nice stats there. The full report can be requested from bizdev@sharethis.com (Unfortunately, I cannot post it directly here.)

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? :-)

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.

Six Degrees of Bret Taylor

I have been collecting a crawl starting from one of FriendFeed's founders Bret Taylor's subscriptions. In some of the previous posts, I have shared an initial analysis of this crawl. After the crawl came to a completion, I have found about 70,535 users who are either directly or indirectly connected to Bret. This leads me to believe that the true user population of FriendFeed might be around 75-80,000 range (also similar to the numbers found by crawling the public users). I think FriendFeed is still mostly an early adopter tool and hasn't gone as main stream yet.

Well, if you are curious what your bret number (six degrees of Bret Taylor) is, check out this zoho sheet and search for your name by clicking the Auto Filter.

FriendFeed CommentRank

Comments are the essence of FriendFeed. For the partially completed crawl, I also started extracting the recent posts a user has commented on (or liked). All this information is available via the FriendFeed API. For a graph of about 22,000 users' *recent* comments network I ran the PageRank algorithm. Interestingly, the ranking is different again from the previous subscription only graph. I think CommentRank emphasizes more on the users who in the recent past have been active and surfacing cool posts that have attracted the community's interest. Hence it seems like CommentRank on FriendFeed might actually be quite fleeting! Here is a list of top 10 users according to CommentRank.

1. Igor The Troll
2. Mona N
3. Robert Scoble
4. edythe
5. Noah David Simon
6. Hao Chen
7. Shey
8. Tad Donaghe
9. Thomas Hawk
10. Mark Trapp

Again, these results are fresh of the grill and the crawl is still incomplete. As I tweak the algorithm and more crawl becomes available -- I guess, these rankings are bound to change. Although, Robert Scoble would be mighty glad to know that he has made it on both the lists! Wow! How does he manage to keep up with all that information?!

This gives me an idea to hack up a neat tool that shows the interesting FriendFeed users from the recent history. I think it would be a nice tool to help find new people to follow. Might actually do this if I find some time....

For now, this list shows the users as ranked purely based on the comment graph. However, I believe that the right way is to rank them is by combining the comment graph and the subscription graph. My reasoning is as follows:

"Influential/Interesting users on FriendFeed are those who can attract comments from people of importance/authority/high-pagerank in the network."

I think the nice thing about this dataset is that for the first time we can actually define as well as tangibly measure influence in social media as the ability of a user to attract attention (in the form of comments) not just by a lot of people but more importantly from authoritative users. This I call the InfluenceRank as opposed to the CommentRank (based on the comment graph) or the SubscriptionRank (based on the subscription graph). I think I would need to write some more code to get the Influential Users on FriendFeed i.e a modified version of PageRank that works on the comment graph but distributes the weights at each node based on the importance found using the subscription network.

This dataset is just fanscinating and exciting!