Some of you might be interested in this:

http://blog.linkibol.com/post/How-to-Build-a-Popularity-Algorithm-You-can-be-Proud-of.aspx

On Fri, Oct 2, 2009 at 16:43, Nick Arnett <nick.arn...@gmail.com> wrote:

>
>
> On Fri, Oct 2, 2009 at 1:00 PM, David Fisher <tib...@gmail.com> wrote:
>
>>
>>
>> For the most part its just a frequency count of words over a short
>> time period, minus stop words, filtering out usernames (notice @foo is
>> never a trend) and URLs. How it combines "Wave OR Google Wave" I'm
>> unsure of, and then there's some basic spam filtering in there
>> additionally.
>
>
> I hope it isn't that naive -- do you know what they're doing, or are you
> speculating?
>
> For one thing, systems that count the unique individuals mentioning a term,
> rather than just raw term counts, are far more accurate in predictive
> modeling.
>
> Furthermore, Twitter has plenty of data to incorporate traffic and social
> network analysis to further improve this "buzz" analysis.
>
> FYI, I've been doing social network buzz analytics for about ten years and
> have some patents in that area (which don't belong to me, but to
> Nielsen/Buzzmetrics).
>
> Nick
>
>


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