Why Twitter Clients still lack Classification, Clustering and Ranking?
On Facebook the average user has about 130 friends and I believe that the average user of Twitter follows a similar number of people.
Considering one or two Tweets per day from each, plus 10 or more from accounts like CNN or ABC, it’s reasonable to think that you would have to look at 250 messages per day. And if you follow more people, or automated services, this number is even higher.
Who has the time to read all this? I doubt you will keep looking at your Twitter stream the entire day looking for those few gems. And if you do, most days you will learn that Bob is sipping a cappuccino and Jane bought new boots, instead of something really useful.
So I wonder: why Twitter clients (e.g., TweetDeck, Seesmic, …) do not learn from news products (e.g., Google News) and start adding classification, cluestring and raking of followers/tweets?
Classification would help probabilistically flagging tweets I may care about (e.g., technology, search, …) from the ones I do not (e.g., you are watching Lost, eating a pizza, …) pretty much like the spam filters do in modern email clients.
Clustering will put together all the tweets/discussions about the same topic (e.g., comments on the new movie of Bruce Willis) so I do not see them scattered in pieces here and there and I can quickly understand what is the general opinion on it.
Ranking could then take advantage of both classification and clustering, understand who I care the most among the people I follow, and rank the tweets in my stream accordingly.
With 41 Million tweets per day (39% containing link, for the majority spam) we could already take advantage of smarter Twitter Clients.
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