On Friday I came across this tweet from Brian Frank stating he had switched his Lifestream to a Likestream. Lifestreams normally contain a mashup of both personal content we create as well as content we find interesting that we “like” and share. I found his decision to basically remove his personal content from the equation pretty interesting, insightful, and a sign of something I’ve been paying close attention to.
Over the last few months I had shifted my thoughts from the methods we aggregate and display our personal lifestream data to better ways to consume the data we are all putting out there. When I went to SXSW back in March of this year I shared my thoughts about this with many of the people I know who are developers, startups, and the like in the lifestreaming / data aggregation sector. I got a warm reception from many of the folks I spoke to about this topic and even had a discussion with Robert Scoble, Mona Nomura, and Mark Rizzn while I was over there.
Here’s an excerpt from that talk that discusses this topic:
When I returned from SXSW I put my thoughts at the time into a post discussing how to build a content reader that displayed all the information my friends were sharing. Over the last year we’ve started to see a huge surge in tools and services that allow us to share objects we like socially and we’re seeing large numbers of users adopting them. So I think the data to help fuel this type of reader is actually starting to get even better. There are several key things I can point to that I feel are propelling the movement of fine tuning all of us into effective content sharing recommendation engines.
A big shift in the tide came when Facebook released the “Like” button, back in April. While it seems very simple it provided a major mental shift for all of us moving from the verb Fan to Like. This would also set the stage to allow us to easily create “Like” touch points for almost any content on the web that would proliferate beyond Facebook like wildfire. This universal button and gesture has created a simple and effective way for us to highlight content making it much easier than using functionality which differs and is isolated when you do it natively on a service.
Another method that is providing us with the ability to share in new ways is the action of checking in or sharing what we’re doing besides just locations which Robert discussed in the interview above. There are several services that are pioneering this including GetGlue, Miso, and Hot Potato which was just bought by Facebook. You can get a good comparison of these services on a recent post by Digi Jeff here. I’ve just recently started to experiment with Getglue and their mobile app now available on Android as well as iPhone. In the case of Getglue I can get recommendations based on my like activity and I can view the activity and likes of friends on their profile pages. There’s also a stream page where I can view activity, but I can’t get a well organized aggregated view of all likes based on media types on a single page from the people I follow which I feel could make for a more compelling page.
Recently Flipboard got a lot of attention because they provided a beautiful interface to view the content generated by those you follow on Facebook and Twitter. While this was nice, you can’t define the logic used to display the content based on the popularity of multiple friends sharing something or any other filters to customize who’s content you want to view. I mention Twitter Times (my page) often as a tool I use daily that does offer me a view based on the number of friends linking to a story or other piece of content. There are also other tools that provide interesting ways to consume content shared by friends that are coming like Paper.li (my page)
Creating more ways to share all the things we consume and like is definitely something that will continue to evolve across the web. This is definitely an area that will get traction as there are huge monetization strategies that can be applied to this data we’re generating. Pair this new age of liking with the ability to break down content by media types, categories, social graph, influence and other variables and you will see some very compelling things coming from this data soon.