People consume news because they need to be informed about the state-of-the-world and events around them. As consumers do not have access to the original information it is difficult for them to assess the credibility and veracity of news. They need to trust on news producers and intermediaries.
Winning consumers trust is becoming the central issue of our times as businesses compete for attention in a digital world, but consumer trust has been declining in the news media, being currently at its lowest point. The uprising of the “Fake News” phenomenon has had a deep effect on the news media industry and our societies, provoking that only 44% of the population shows some trust on news. ‘Fake news’ gained attention in the aftermath of the unexpected outcomes of electoral contests in Western Europe and North America in 2016, but ‘fake news’ is not simply ‘false’ news but also disinformation. Disinformation is a phenomenon that goes well beyond the term «Fake News». This term has been appropriated and used misleadingly by powerful actors to dismiss coverage that is simply found disagreeable. Disinformation as defined by the High Level Experts Group set up by the European Commission, includes: “all forms of false, inaccurate, or misleading information designed, presented and promoted to intentionally cause public harm or for profit”. Disinformation travels faster and further on social media sites. Algorithm-driven news distribution platforms have reduced market entry costs and widened the market reach for news publishers and readers. At the same time, content-curation algorithms have been designed to maximize traffic and advertising revenue not news accuracy. This situation weakens the role of the traditional gatekeepers (media industry) as quality intermediaries and facilitates the distribution of false and fake news content.
Newtral aims to leverage deep learning, speech recognition and NLP technologies to fully automate the verification of fake data but it also applies a community-driven approach when automation is not effective. We are building the only end-to-end global and scalable solution to verify fake data in the market.
The project objectives were:
Technical and Practical feasibility (SO1): We will review the state of art of involved technologies to define an initial technical roadmap for an end-to-end solution for automated fact-checking. Evaluation of blockchain as enabling technology to decentralise the process, create the marketplace for fact-checking and guarantee information traceability.
Commercial and Business feasibility (SO2) Analysis of target markets. As part of the feasibility study we will review main stakeholders, customers and competitors in potential markets, paying special attention to EU, USA and Latin America, to identify potential partners, mostly active fact-checkers and reference customers or partners. Stakeholders’ analysis and strategic alliances. Validate the community-driven model. Organise small focus groups with journalists, professional fact-checkers and news media managers to discuss platform features, define the self-governance protocols and the exploitation model (verified new label).
Financial feasibility (SO3) Adjustment and fine-tuning of business model. Adapting financial plan to target market segments, prices and operational costs. Revenue projections. Capital requirements, cost-benefit analysis, financing options and ROI. Elaboration of new business plan based on results.
