In the last round, back in July, we saw a significant uptick in interest in fact checking projects. That trend continues in this round, especially in the prototype project category. In the medium and large categories, we encouraged applicants to focus on monetization, which led to a rise in medium and large projects seeking to use machine learning to improve content delivery and transform more readers into subscribers. Overall, 21 percent of the selected projects focus on the creation of new business models, 13 percent are about improving content discovery by using personalisation at scale. Around 37 percent of selected projects are collaborations between organizations with similar goals. Other projects include work on analytics measurement, audience development and new advertising opportunities. Here’s a sample of some of the projects funded in this round:
[Prototype] Stop Propaghate - Portugal
With €49,804 of funding from the DNI Fund, Stop Propaghate is developing an API supported by machine learning techniques that could help news media organizations 1) automatically identify if a portion of news reporting contains hate speech, and 2) predict the likelihood of a news piece to generate comments containing hate speech. The project is being developed by the Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), a research & development institute located at University of Porto in Portugal.
[Medium] SPOT - France
Spot is an Artificial Intelligence-powered marketplace for curating, translating and syndicating valuable articles among independent media organizations, and is being developed by VoxEurop, a European news and debate website. With €281,291 of funding from the DNI Innovation Fund, Spot will allow publishers to easily access, buy and republish top editorial from European news organizations in their own languages, using AI data-mining technologies, summarization techniques and automatic translation technologies, alongside human content curation.
[Large] ML-based journalistic content recommendation system - Finland
Digital news media companies produce much more content than ever reaches their readers, because existing content delivery mechanisms tend to serve customers en masse, instead of individually. With €490,000 of funding from the DNI Innovation Fund, Helsingin Sanomat will develop a content recommendation system, using machine learning technologies to learn and adapt according to individual user behavior, and taking into account editorial directives.
The recipients of fourth round funding were announced at a DNI event in London, which brought together people from across the news industry to celebrate the impact of the DNI and Innovation Fund. Project teams that received funding in Rounds 1, 2 or 3 shared details of their work and demonstrated their successes in areas like local news, fact checking and monetization.