Global Investigative Journalism Network (https://archive.gijn.org/tag/machine-learning/page/2/)
Using image analysis tools on their own means nothing without asking the right questions. AJ Labs experimented with Google Vision API, a machine-learning technology, to analyze images used by Al Jazeera over the course of last year. They hope to integrate the technology into their newsroom in the future to make processes easier for their journalists.
What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from November 18 to 24 finds Google Trends mapping the busiest places and times for Thanksgiving, Al Jazeera profiling the work of data journalist Mona Chalabi and her hand-drawn visualizations, the Tow Center for Digital Journalism examining the use of machine learning and artificial intelligence in journalism, and the Investigative Reporting Workshop collating useful public datasets in one place.
There are two ways to use machine learning in journalism: as part of investigative reporting, or as a day-to-day tool to make journalists’ lives easier. But the journalism industry is still scratching the surface when it comes to machine learning and deep learning.
What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from June 4 to 10 finds an awesome curated list of resources for visualizing music by @Willian_justen, a deep dive into unsolved murders across America by @washingtonpost and @CarbonBrief mapping of the past, present and future of global coal power plants.
What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from Aug 20 to 26 finds @Numerama analyzing the representation of women in @netflix series and finds it lacking, @AJEnglish creating an interactive explainer on how 2 million Muslims make the pilgrimage to Mecca every year, @FT readers brainstorming air pollution solutions for London, and @Bastamag digging into data of @Airbnb — a cash cow for home renters.
What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from December 31, 2018 to Jan 6, 2019 finds experts sharing their thoughts on machine learning in journalism with @storybench, @funkeinterativ and @webk1d’s useful tool to merge datasets, @pewresearch’s overview of female under-representation in online image searches, and an analysis by @EDNNews on corpses bodies sent back to El Salvador.
What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from April 8 to 14 finds a zine focused on machine learning-powered investigative journalism produced by @bxrobertz, a video explainer from @FT on whether big corporations are really generous or just avoiding taxes, @knowtheory and @amandabee reviewing seven optical character recognition tools and @workbenchdata offering a tutorial on visualizing @Twitter data.
Artificial intelligence for journalism is both promising and overhyped. Computational journalist Jonathan Stray dug into the state of AI in investigative reporting, outlining for GIJN where the near-future opportunities lie.
What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from October 21 to 27 finds a panel of leading data visualization practitioners discussing the practice of visualization in an age of disinformation, Kloop exposing how Kyrgyzstani authorities privatized large swaths of a public park with no oversight, and The Guardian highlighting the minimal changes between former British prime minister Theresa May’s Brexit deal with current PM Boris Johnson’s new deal.
What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from Sept 11 to 17 has @puntofisso using machine learning to map the average face of British politicians, @AftertheFloodco creating a font that turns numbers into sparklines, and @datassist listing seven deadly sins in statistical analysis.