Perhaps of interest, especially to news researchers and folks interested in tapping Google's Big Query tools.
TJ ============================================ Tom Johnson Institute for Analytic Journalism -- Santa Fe, NM USA 505.577.6482(c) 505.473.9646(h) Society of Professional Journalists <http://www.spj.org> *Check out It's The People's Data <https://www.facebook.com/pages/Its-The-Peoples-Data/1599854626919671>* http://www.jtjohnson.com [email protected] ============================================ ---------- Forwarded message ---------- From: kalev leetaru <[email protected]> Date: Wed, Apr 26, 2017 at 10:53 AM Subject: [ddj] new api for mapping the geography of global news coverage To: "List about Data Driven Journalism and Open Data in Journalism." < [email protected]> Apologies for cross-posting - thought many on this list might find of great interest and utility the new GDELT mapping API for creating point, ADM1 and country-level maps of the geography of global news coverage from nearly all countries worldwide in 65 languages over the last 24 hours, updating every 15 minutes. The API, which is fully free and open, generates both instant embeddable browser-based interactive maps and GeoJSON optimized for use with platforms like Carto. http://blog.gdeltproject.org/gdelt-geo-2-0-api-debuts/ Specify any keyword or phrase and search the English machine translations of all content monitored in those 65 languages, allowing you to search across languages. For each keyword, the system compiles a list of all locations (down to the resolution of a hilltop in many areas) that were found within a sentence or two of your keyword and constructs a map showing the locations mentioned most frequently in context with your search. You can also map specific languages, domains, by tone, etc. Perhaps most uniquely, we are releasing a set of experimental maps that apply the deep learning image categorization we perform on global news imagery each day (more than a quarter billion images processed last year) and let you search by 10,000 labels of objects and activities depicted in the image, the OCR'd text in more than 80 languages depicted in the image, all of the text contained in the image file's metadata fields, the textual caption of the image as it appeared in each article, and the result of a Google Images reverse search that compiles a list of all of the captions used for that image anywhere it was found on the open web and assigns several million topical labels. As but one simple, but extremely powerful example - one of our research threads revolves around how violence is depicted across the world and the differing levels of normative baselines (for example, here in the US ISIS beheadings are typically shown with a "before" image or a heavily pixelated image, whereas in the presses of certain other countries the raw graphic image is frequently shown; similarly in the US we rarely see imagery of drowned refugees with the notable exception of Alan Kurdi, while the presses of other countries run graphic imagery of those who perish on a more frequent basis). Understanding how violence is depicted in the presses of the world and how those baselines are changing offers a lot of insight into the question of desensitization and the communication of crises. The new API allows you to create such a map in just a few seconds and have it live update every 15 minutes - here is one such example map that displays up to five images from the domestic press of each country over the last 24 hours that were determined to potentially depict some sense of "violence" (click on each country to see the images from its local press). (WARNING: many of these images are very disturbing). While you will see some errors here and there, overall this gives a very visceral sense of the differences in depiction of violence throughout the world. http://api.gdeltproject.org/api/v2/geo/geo?query=imagetag: %22safesearchviolence%22&mode=imagesourcecountry&format=imagehtml Similarly, here is a map of rubble, destruction, flooding and fire that we are using in a series of forthcoming projects to ground truth the severity of natural disasters as they occur in realtime (note that this particular map below shows the imagery of damage FROM the press of that country, which may reflect events in other countries as well): http://api.gdeltproject.org/api/v2/geo/geo?query=( imagetag:%22rubble%22%20OR%20imagetag:%22demolition%22% 20OR%20imagetag:%22disaster%22%20OR%20imagetag:%22earthquake%22%20OR% 20imagetag:%22flood%22%20OR%20imagetag:%22fire%22)&mode= imagesourcecountry&format=imagehtml Looking at text, you can map a particular news outlet like AllAfrica: http://api.gdeltproject.org/api/v2/geo/geo?query=domain:allafrica.com Or source language like Chinese: http://api.gdeltproject.org/api/v2/geo/geo?query=sourcelang:chinese Or the phrase "Donald Trump", aggregated to the country level: http://api.gdeltproject.org/api/v2/geo/geo?query=%22donald%20trump%22&mode= country You can find many more examples, along with full documentation on the announcement this morning: http://blog.gdeltproject.org/gdelt-geo-2-0-api-debuts/ Feel free to email me directly with any questions! We are super excited to see what you all are able to do with these new capabilities! And stay tuned for our temporal API being released in a few weeks! Kalev http://blog.gdeltproject.org http://kalevleetaru.com/ _______________________________________________ data-driven-journalism mailing list [email protected] https://lists.okfn.org/mailman/listinfo/data-driven-journalism Unsubscribe: https://lists.okfn.org/mailman/options/data-driven-journalism
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