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→Inspiration, or the "Dream Case"
===Inspiration, or the "Dream Case"===
**'''WHAT IF WE''' tap on NLP capabilities to monitor twitter handles that are known to be important, and have a constant feed of important rising new '''words''', rising new '''mentions''' and rising new '''hashtags'''. Using this feed, we can populate and update graphs constantly, measuring ''''delta'''' instead of using graph data per se, and thus develop a good grasp of rising organizations, events and startups in the twitterverse. We would know things before other people do. Value.
***Empirically, and in a micro way, I have observed that a new startup known as '''Aminohealth''' [https://twitter.com/aminohealth @aminohealth] (enables end-users to shop around for doctors based on price range; seems very novel and in-demand) has been appearing very constantly on important feeds such as '''@techcrunch''', '''@redpointvc ''' and '''@accel'''. It has just received a ''''huge launch'''' but is relatively unknown in the bigger twitter picture. There is also nothing conclusive about what this launch entailed, and what kind of funding it received. Using the NodeXL tool, we can conceivably find out everyone that's involved in @aminohealth's recent activities, and systematically mine knowledge from this network. ***'''@aminohealth ''' itself possesses only around 1,000 followers, despite having 700+ tweets. '''Delta''' is far more important than what-is for rising startups as such.[[File:Capture 25.PNG|600px|none]]
***Empirically, the twitterverse is populated by important organizations as well as, we often forget, their staff. @jflomenb is constantly mentioned by @redpointvc and @accel, and has interesting exposes information about the entrepreneur scene, as shown. Again, delta is crucial.
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**'''What if we''' compare social networks against themselves over time?
***If we generate useful network graphs and data '''OVER TIME''' that revolves around a single entity e.g. '''@redpointvc''', we would be able to do a few pretty amazing statistical analyses:
****The mean number of mentions before a startup gets signed to a VC
****What are the quantitative tweet indicators that a startup is succeeding/failing?
****What are the qualitatively trendy topics that are mentioned in the history of a VC? Does this influence their activity, if at all?
****Any regression for the above, and more
**'''What if we''' track ongoing events such as '''#kpceoworkshop '''
***It'll be easy to find out who are the people that are attending the workshop, and add them to our watchlist of important people
***Also, how important or impactful are these events? We can track their mentioners and likers and followers to identify and think about follow-up events that happen occur after the events themselves conclude. (hmm..)
===Limitations===
