Many Ways I Use Twitter

I am on and off on Twitter. But of late I am more on. Here is why. I found a bunch of uses of Twitter.

1. As a bookmarking tool (I started using it in addition to del.icio.us)
2. As a source for instant tech info - I follow some cool dudes like Dave Winer, Robert Scoble, Jon Udell, Dion Hinchcliffe, eHub and zdnet. I get a lot more than what I can handle in any given day
3. As a source of instant social news - I follow a bunch of friends and as the twit, I get hit
4. As an idealog - I just started this. Throw out ideas and someone will respond. A quick validation. Previously I used to blog about it (takes too much time), write it down (forget where it is), put it in a wiki (again takes too much time). I know that I can take these idea stream and work on some of them at some point in time.
5. I think some really cool stuff is happening with twitter as a collaboration tool. I followed Dion and team building a social graph app on Google engine (heard it through Dave’s posts)
6. Talk to myself. Read-pause-reflect an eternal spiral.
7. As a LearnLog (what did I do, why did I do it and when did I do it). Always amusing to see why work seems so much fun and after some time realize that you are kind of working but mostly having fun.

I think I will set some self imposed limits so that I do not become an addict. I haven’t figured out what they are yet. Here is my Twitter Account.

Update: The original title was Seven Ways I use Twitter. Then I though, why limit it? Why not keep adding new ones?

8. Purely for fun - it is kind of intoxicating

9. Just getting out “Ideas Worth Spreading” - a tag line of TED Talks, I love

10. As an advertisement for my blog? Mixed feelings about that.

Musings: More News Than Comment

Every time I write a blog post, I think about this. Am I posting more news than comment?

The urge to share is a powerful one. I do it in different ways - del.icio.us, posting to Facebook and a bit of twittering. I still, have not figured out my urge to blog. Most of the time it is news with an occasional comment. Sometimes there is more comment than news.

I like the way Peter Suber says it.

This blog is more news than comment. For more comment than news, see my writings on OA.

Research: How Do You Find What Blogs to Read?

Research quesion: What blogs should you read, to be up to date on newsworthy stories?

Given a budget of 100 blogs, the biggest bang for the buck belonged to the popular Instapundit blog, which featured more than 4,500 postings throughout the year. Assuming a budget of 5,000 posts, however, the top-scoring blog was the less well-known sisu site, which featured only 331 posts for all of 2006.

How do you find something like this? How do you even go looking for this information?

During the past couple of weeks, I have been reading about Sentiment Analysis. It was initiated by a post in the Text Analytics mailing list by Seth Grimes and followed by many good posts with links. I read a few, generally understood the concept. It is a fascinating idea. More on this in a later blog post.

I came across this article today. It is some what different, but very useful. How do you find what blogs to read. As a person who is constantly buried by information overload, this is a question I always ask myself. Looks as if some interesting work is going on in Carnegie Mellon Researchers. The result? An algorithm called Cascade.

A team of researchers and graduate students from Carnegie Mellon eventually created a complex mathematical equation called the cost-effective lazy forward-selection algorithm, later dubbed the Cascades algorithm for simplicity’s sake.

One part seeks to maximize reward, in this case detecting the most news in the least amount of time. Within the algorithm, that reward concept is captured by tallying the number of people who read a news item after it appears on a specific blog. If 10 million people read a story after its initial posting on Blog A but only 1,000 had read it beforehand, the story would be deemed both newsworthy and early-breaking for Blog A’s readers.

A second part of the algorithm seeks to minimize cost, namely the inordinate time that could be spent reading blogs. The team also exploited a mathematical relationship known as the law of diminishing returns.

This algorithm is not only useful for detecting news worthy blogs, but also water pollution. The sensors are just different.

You can read it all in this story on msnbc From ranking blogs to predicting posture.

meta: stumble path

acm technews -> article about cascade algorithm