No offence but I got to check out www.kenyatweets.com and I just couldnt tell what was so different from Trendy Desktop < https://sites.google.com/site/jacobayienda/> Looks very much like a clone to me with the only difference being that it is web based. They probably even cloned the blue color too. The top trends and links for one either dont seem to refresh or are taking too long. I also got to look at some report on their website and I just have to say Kenyans really know how to copy ideas. Unfortunately as they will learn as I have, Twitters pretty much killing of 3rd party apps by implementing their unique functionalities or buying off the bigger competitors. There wont be much of a 3rd party market by the time they are finished.
At the same time, I am a bit curious about the sourcing of the tweets on www.kenyatweets.com, rather than an algorithm, they appear to be crowd-sourced where the (crowd ≠ a normal crowd), or rather, (crowd ≈ a few individuals). In comparison, paper.li ie http://www.paper.li/tag/kenya seems to be more efficient and a truly crowd sourced application.
A paper.li collection is defined by the creator so it collates tweets from whomever is put on the list/collection etc, is this crowd sourcing?? V On Tue, Oct 26, 2010 at 12:27 PM, Dennis Kioko <dmbuvi@gmail.com> wrote:
At the same time, I am a bit curious about the sourcing of the tweets on www.kenyatweets.com, rather than an algorithm, they appear to be crowd-sourced where the (crowd ≠ a normal crowd), or rather, (crowd ≈ a few individuals).
In comparison, paper.li ie http://www.paper.li/tag/kenya seems to be more efficient and a truly crowd sourced application.
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well, I guess not :-( , though , in my defence, kenyatweets.com, in my opinion, is based on the fact that the creators define the the community that makes the Kenya twitter community. Interesting case for an A.I/machine learning assignment.
Interesting : kenya_tweets A.I in action. Intelligent tracking <http://blog.kenyatweets.com/2010/10/tracking-the-diaspora/>
*Saddam Hussein* is *kenyan* (51.6308239011%)
hehe. On Thu, Oct 28, 2010 at 3:39 PM, Dennis Kioko <dmbuvi@gmail.com> wrote:
Interesting : kenya_tweets A.I in action. Intelligent tracking <http://blog.kenyatweets.com/2010/10/tracking-the-diaspora/> _______________________________________________ Skunkworks mailing list Skunkworks@lists.my.co.ke http://lists.my.co.ke/cgi-bin/mailman/listinfo/skunkworks ------------ Skunkworks Rules http://my.co.ke/phpbb/viewtopic.php?f=24&t=94 ------------ Other services @ http://my.co.ke
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Well, as I said, it would form an interesting Machine learning assignment, maybe an assignment for Idd Salim <http://www.iddsalim.com/blog/>.
oh crap i got lost in all that jargon...
reality about twitter is that majority of the people on it rarely tweet. we may have 10, 000 kenyans on twitter but only roughly 50 of those tweet on a regular basis. The rest just check their timelines for updates or have dead accounts either way only twitter can tell. Back to my point the kenyatweets does not explain the algorithm they use to get the trending topics as they appear either as not updated or manually inserted. The number of kenyans on twitter is really irrelevant. 1. Some users tweet from more than one account (i have 5 accounts) 2. Some users dont tweet at all 3. Most of the tweets are spam or FB crap! whih has no value at all i.e why would you tell your followers you just woke up if you are not a celebrity The relevance of information in tweets is more important and thus should be the focus of any analysis.
participants (4)
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Dennis Kioko -
Erick Njenga -
Jacob Ayienda -
Victor Ngeny