**3. The heliosphere architecture**

The HELIOSPHERE Engine Architecture is developed in a modular manner to support transparent and inclusive debates [26]. The architecture has four main goals: data collection, machine model training and deployment of the tools, visualisation during and after debates. There are three main parts of the platform: Data Engine, Machine Learning Engine and Customizable Visualisation Engine.

#### **3.1 The heliosphere data engine**

The HELIOSPHERE Data Engine is responsible for storing and pre-processing all the collected data, including Data collected from the debates themselves. During the debate, an automatic speech to text module transforms the speech into text. Additionally, data collected from other sources, including related initiatives, historical events, business/academic, political entities, published speeches (video, audio, transcripts), documents from governmental and non-governmental institutions (including UN, UNESCO, EU Council, EU Parliament, National Legislative Bodies, WTO, World Bank, IMF) and NGO's published data. Data collected from publicly available content from TV and print media, publicly available social media postings (Twitter, Facebook, Reddit, YouTube, Steemit or any relevant or future social media platform) related to the debate topics are pre-processed and stored within the data engine.

Since the data collected is heterogeneous, it requires collecting raw data, which is parsed, pre-processed and standardised to be compliant with reusability and compatibility. The raw data is pre-processed, prepared and annotated before including it in the data storage engine continuously. As such, the technological solution

#### *A Theoretical Concept to Increase the Trustworthiness of Online and Offline… DOI: http://dx.doi.org/10.5772/intechopen.98442*

would necessitate a distributed environment, such as Hadoop1 system to provide real-time queries and interactive aggregations even with tens of thousands of data points. The data engine is structured to provide fast (1–2 seconds query access) to the data, requested either by the ML and visualisation engine, third parties through the APIs or other services. Furthermore, specific blockchain smart contracts need to be included in the Data engine to guarantee data privacy.

To mitigate and recognise fake, deep fake information and illegal content, the engine ensures the utilisation of blockchain technology to provide traceability, transparency, and decentralisation. As such, Blockchain implementation offers reliable support for verifying both the content and its source. Different actors, people involved in the debate, can access a public blockchain where data is tagged and can, in turn, define a 'Debunker Community' and can give opinions on the content during the debate. These opinions may be registered in the tamper-free, publicly accessible ledger. However, complex queries on the blockchain's data cannot be directly supported by the blockchain itself due to performance and scalability issues. HELIOSPHERE, therefore, provides an interface between the blockchain and the Data Engine so that the Data Engine can retrieve the data on the blockchain to support complex data analysis efficiently. The Data Engine will also store the result of complex aggregation queries in the blockchain. This ensures the results of the study available to the actors of the debate and immutable.
