Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - NEWTRAL (First real-time fact-checking tool to fight against the fake news and disinformation)

Teaser

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...

Summary

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.

Work performed

During the project we have completed the following main tasks:
Market analysis: Market and Customer Analysis including risk assessment, identifying key actors, priority markets, market sizing, trends and segmentation. The problem of the fake news and regulation against fake news was also studied. Study the economic damage of disinformation campaigns. Analysis of the fact-checking organisations. Detailed competitor analysis, benchmarking and customer surveys. Identification of Serviceable Market and final obtainable market with 5-year projection. Definition of the strategy for reaching the target international market, arousing customer´s and stakeholders’ interest in the innovative service and pricing strategy. Preliminary commercial expansion plan defining the steps to commercialise our solution taking into account above the risks, market analysis, swot, and technological analysis. Stakeholders engagement and evaluation

Technological analysis: ASR (Automatic Speech Recognition), Claim Detection and Claim Matching solutions, PoC: Crowdsourcing fact-checking, Blockchain as a technology to support fact-checking. Newtral Innovation plan. Risk assessment. Freedom to operate: IP protection strategy and regulations/standards compliance.

Operation analysis: Operational, Technology and Financial requirements analysis including technical staff needs, Editorial staff needs, Sales team needs and technology needs

Final results

The state of the art of computational fact-checking includes a broad range of technologies and initiatives. Up to date, fact-checking solutions have focused on parts of the problem without comprehensively addressing the multiple faces of disinformation and therefore, not providing a holistic solution. Attempts to build end-to-end fact-checking solutions (e.g. FullFact, Claimbuster) have failed because they have not defined a sustainable business model for the exploitation stage neither mechanisms to solve issues when automation is not enough. Newtral is the only initiative that covers the four fact-checking phases while creating a sustainable business model for fact-checkers that implement it. It defines both a roadmap to automate the fact-checking process and a blockchain-based business model based to support the huge community effort needed to create a massive dataset of verified information. 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.

Website & more info

More info: https://www.newtral.es/automated-fact-checking/.