mining images in social sciences
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
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@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 _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
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@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@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 _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
_______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
-- משה קרבלניק
Dear Kalev, Ronald, Moshe and all, Thank you very much for your responses and insights! Any other references to articles or books related to the use of computational methods (alone or in combination with qualitative methods) for the analysis of images in social sciences would be much appreciated. best wishes, Alina On Fri, Mar 22, 2019 at 9:09 PM Moshe Karabelnik <karabelnik106@gmail.com> wrote:
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@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@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 _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
_______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
-- משה קרבלניק
participants (3)
-
Alina Curticapean -
kalev leetaru -
Moshe Karabelnik