[Air-L] mining images in social sciences

[Moshe Karabelnik]

Dear Alina,

After a conversation which started in the AOIR discord channel, Dr James
Allen-Robertson developed a media downloader for twitter that scrapes media
based on a list of tweets. I have recently used it to gather images
uploaded to twitter during the launch of a new product by a major tech
company (which I consider a new event) and it has worked perfectly
providing useful information which I am currently analyzing.

This is Dr Allen-Robertson original posting in the AOIR newsletter:

*"*
I’ve developed a small command line tool for downloading media from
Twitter. Discussions on the AOIR digital methods Discord channel
demonstrated a demand for such a tool so I went ahead and pulled something
together.

The script (Python based) takes either a list of tweet ids or an exported
data table generated using the TwitterStreamingImporter plugin in Gephi,
and retrieves all embedded photos, videos and animated gifs.

It is currently relatively bare bones, but it has an extensive guide to
support those unfamiliar with running Python scripts. If you have any
issues/feature requests feel free to email me or log an Issue on Github.

The tool can be downloaded from...
https://github.com/Minyall/gephi_twitter_media_downloader
*"*

I am very interested in the practice of using visual media, I will go
through my folders and try to find relevant articles for you. If you would
like to collaborate please feel free to send me an email.

Moshe Karabelnik


On Fri, Mar 22, 2019 at 3:29 AM kalev leetaru <kalev.leetaru5 at gmail.com>
wrote:

> Alina, if the imagery dimension is more important than the social media
> dimension (and noting that media often republishes the most iconic imagery
> from social), our open data catalog of global online news imagery
> 2015-present (around half a billion images totaling a quarter trillion
> pixels and around 300 billion computed datapoints) might be of great
> interest:
>
> https://blog.gdeltproject.org/vgkg-2-0-released/
>
> Each day it scans the images from online news coverage worldwide and
> selects a random sample of around 700K images a day to run through Google's
> Cloud Vision API to create a full annotated metadata record with the URL of
> the image, the URL of the first article it was seen in, and huge amount of
> computed metadata, from 10K+ visually assigned labels, 2M+ entities
> computed from its textual captions everywhere it appeared on the web across
> languages, its estimated geographic location if recognizable, whether it is
> likely to depict violence, the average facial emotion, OCR of all text in
> the image in their respective languages (this includes protest signs), all
> EXIF/IPTC/XMP metadata in the file itself, all of the other locations on
> the web the image or any piece of it appears (essentially a reverse Google
> Images search) and a huge range of other attributes. This can be linked
> against our main knowledgegraph to find all of the other articles the image
> appeared in, allowing you to compare textual and visual narratives.
>
> Kalev
>
>
> On Fri, Mar 22, 2019 at 8:43 AM Alina Curticapean <
> alina.curticapean at gmail.com> wrote:
>
> > Dear fellow members,
> >
> > I am planning a research project which aims to study political
> > participation by visual means. More precisely, the project plans to
> collect
> > images related to e.g. political protest, immigration, animal rights from
> > social media and use computational methods to mine them. I am a real
> novice
> > in what concerns mining images in social sciences and I would need your
> > help to guide me to the relevant literature. I would just add that text
> > data will be collected as well, but I am quite familiar with mining
> methods
> > for text data (NLP).
> >
> > Looking forward to helpful advice to get me started with the project I
> wish
> > you all a nice weekend!
> >
> > With the best wishes,
> >
> > Alina
> > _______________________________________________
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-- 
משה קרבלניק