[Air-L] Please assist a grad student on using SNA to study pre- and post-disaster social networks? (Yosem Companys)

[Deen Freelon]

Hi all,

Anyone who's interested in converting free-text location fields into 
standardized location data might want to look at a Python module I 
designed to do just that: https://github.com/dfreelon/geostring

You simply feed it a list of locations and it tries to guess where each 
one is at the country, sub-country (state/province), and city levels. 
I'm interested to see how people might use it.

Best, /DEEN

On 2/22/2019 10:23 AM, Sugar, Benjamin N wrote:
>   Is that 100% of geolocated Tweets in the hours before a disaster? That would be an interesting idea, but a tough one to execute.
>
> Stu’s point is very true.  When the GPS data was not available in the Tweet, we used the location field of the Twitter user’s profile and OpenStreetMap’s Nominatim which gives (or used to give) you a probability of where your query was located.
>
> Of course, not all users have an accurate location field,some are jokes or fictitious places (e.g. Hogwarts) but we got enough state level data for a paper out of it.
>
> https://github.com/openstreetmap/Nominatim
>
> https://wiki.openstreetmap.org/wiki/Nominatim
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-- 
Deen Freelon, Ph.D.
Associate Professor
School of Media and Journalism, UNC-Chapel Hill
http://dfreelon.org | @dfreelon <https://twitter.com/dfreelon> | 
https://github.com/dfreelon