[Air-L] Methods to infer Twitter data location

Thu Feb 23 11:29:13 PST 2023

For what its worth, while GPS-tagged tweets continue their slow descent,
Place-tagged tweets were on an upward trajectory through when we last
looked at the end of last year, though users with a populated Location
field were continuing to decline:

https://blog.gdeltproject.org/visualizing-a-decade-of-twitters-evolution-jan-2012-nov-2022-musks-purchase/

Kalev

On Thu, Feb 23, 2023 at 9:36 AM Jeff Hemsley via Air-L <
air-l at listserv.aoir.org> wrote:

>
> A while back we used the concept of "Place" from Human Geography in a
> paper where we used the profile listing of where someone claims to be or be
> from. I've included the cite for the paper below.
>
> Yes, people do claim to be from Mars sometimes, like Libby says. I expect
> that is because they wish to preserve privacy or they just enjoy being
> silly. Both are ok in my book. And like Libby, I think if your methods take
> into consideration a certain amount of noise, then it should be fine.
>
> Unlike Dan, I'm not to worried about infringing on someone's privacy when
> using the place they list on their PUBLIC Twitter profile.
>
> Here is the paper I mentioned:
> Hemsley, J. J., & Eckert, J. (2014, January). Examining the role of"
> place" in twitter networks through the lens of contentious politics. In
> 2014 47th Hawaii International Conference on System Sciences (pp.
> 1844-1853). IEEE.
>
>
>
>
>
> -----Original Message-----
> From: Air-L <air-l-bounces at listserv.aoir.org> On Behalf Of Gardner,
> Daniel via Air-L
> Sent: Wednesday, February 22, 2023 3:34 PM
> To: Libby Hemphill <libbyh at umich.edu>; Shulman, Stu <stu at texifter.com>
> Cc: aoir list <air-l at aoir.org>
> Subject: Re: [Air-L] Methods to infer Twitter data location
>
> Hi list,
>
> I have been having a nice chat with Deena about this, but given now a few
> technical responses to this query, I wanted to send to list.
>
> I would urge researchers in this space to make sure they are considering
> the immediate ethical dimensions of attempting to infer the physical
> locations of users who have, by their settings and practice explicitly
> said, "please don't know my location," especially if that location may in
> fact be their home...
>
> What that location becomes linked to in any data set produced, could
> potentially be deeply harmful, or at a minimum invasive.
>
> Best of luck,
> Dan
>
>
>
> From: Air-L <air-l-bounces at listserv.aoir.org> on behalf of Libby Hemphill
> via Air-L <air-l at listserv.aoir.org>
> Date: Wednesday, February 22, 2023 at 12:13 PM
> To: Shulman, Stu <stu at texifter.com>
> Cc: aoir list <air-l at aoir.org>
> Subject: Re: [Air-L] Methods to infer Twitter data location External
> Message
>
>
> [External Message]
>
> Depending on your tolerance for error and how specific you need the
> location to be, you may be able to use the user's location (from their
> profile, available through the users API if you still have access) rather
> than the tweet's geolocation. People usually tweet from "home".
>
> Libby
>
> On Wed, Feb 22, 2023 at 2:38 PM Shulman, Stu via Air-L <
> air-l at listserv.aoir.org> wrote:
>
> > User location on Twitter is very tricky and unreliable. Users must
> > opt-in to share geo or to "claim" a location when they set up or edit
> > their account profile. Claims of location range from 100% authentic to
> > preposterous satirical banter and everything in between. Generally, if
> > you restrict yourself to the study geo-located or location-purported
> > data, you are looking at a small fraction of the whole dataset. For
> > example, here are the top most common 30 "claimed" locations in a
> > dataset of Tweets about "Dominion Voting Systems" from November 2020
> > where the most common from
> > 421,000 Tweets is 15,000+ (United States) and the 30th is almost 700
> > (New York, NY). There are 239,051 purported locations in this dataset.
> >
> > United States
> > USA
> > Texas, USA
> > Florida, USA
> > California, USA
> > Texas
> > Georgia, USA
> > Canada
> > Venezuela
> > New York, USA
> > Pennsylvania, USA
> > Arizona, USA
> > Los Angeles, CA
> > Michigan, USA
> > Ohio, USA
> > North Carolina, USA
> > Florida
> > New Jersey, USA
> > Washington, DC
> > Houston, TX
> > Virginia, USA
> > California
> > Tennessee, USA
> > South Carolina, USA
> > Atlanta, GA
> > Washington, USA
> > Earth
> > Illinois, USA
> > Chicago, IL
> > New York, NY
> >
> > To study data like this you may need to concatenate things that are
> > similar but the same in practice at the geographical scale you are
> working with.
> > Some locations are just Twitter handles. Others are Gab or Parler
> handles.
> > Some look like this:
> >
> > A Desk in Open Office Hell
> > A Field of White Roses
> > A Galaxy Far Away
> > A Galaxy far far away
> > A Galaxy far, far South
> > A Garden of Feelings &Hot Air
> > A Getsemani, Jerusalen
> >
> >
> > On Wed, Feb 22, 2023 at 11:32 AM Deena Abul-Fottouh via Air-L <
> > air-l at listserv.aoir.org> wrote:
> >
> > >    Hi aoir,
> > >
> > >    Could anyone please share resources on methods/applications to infer
> > >    Twitter data locations from non-geo-located messages?
> > >
> > >    Thanks.
> > >
> > >    Deena
> > >
> > >    ____________________________
> > >
> > >    Deena Abul-Fottouh, PhD.
> > >
> > >    Postdoctoral Research Fellow
> > >
> > >    Digital Society Lab
> > >
> > >    Political Science Department
> > >
> > >    Phone icon t phone: [1](905) 525-9140
> > >    Envelope icon email: [2]abulfodm at mcmaster.ca
> > >
> > >    McMaster University
> > >
> > >    1280 Main Street West
> > >
> > >    Hamilton, ON L8S 4M4
> > >       McMaster University Brighter World logo
> > >
> > > References
> > >
> > >    1. tel:+19055259140
> > >    2. mailto:abulfodm at mcmaster.ca
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> > --
> > Dr. Stuart W. Shulman
> > Founder and CEO, Texifter
> > Editor Emeritus, *Journal of Information Technology & Politics*
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