## 4 Discussion  
In recent years, the amount of data (documents, images, videos, etc.) that are produced daily has increased tremendously. This has raised many challenges in different fields, such as journalism, where the development of new methods that are able to collect and extract useful information from these vast amounts of information is necessary. Journalistic practices, such as data-driven journalism, follow four basic steps toward the creation of a news story, which are: (a) the data collection, (b) data filtering, (c) data visualization, and (d) story generation. Data collection refers to the collection of raw data while data filtering refers to the process of filtering and maintaining only the information relevant to the news story. Data visualization is the process of transforming data in such a way and creating visualizations to help readers to understand the meaning behind the data. Finally, story generation (publishing) refers to the process of creating the story and attaching data and visuals to the story. However, in each of the aforementioned steps, several difficulties are raised. Multimedia data collection can be laborious due to the huge amount of available data and to the  
Figure 4: Additional view presenting the automatically personalized generated text. multiple data sources. Moreover, in many cases, programming knowledge may be required in order to collect data from certain sources. Additionally, the collected data may come in different specialized formats and may be difficult to be checked and filtered from a person or a group of persons. Furthermore, much of the collected information may refer to fake news that has to be filtered out. In addition, the extraction of additional knowledge from data, such as the EO data, and the creation of easily understandable visualizations may require expert knowledge. Finally, the combination of the available data and the limited response time for publishing breaking news makes it a very challenging task. The whole process is a time-consuming procedure that requires the development of systems that can tackle the above difficulties. Towards this scope, this paper presents an innovative Web 3.0 platform, called EarthPress, aiming to facilitate professionals of the Media industry by automating many of their journalistic practices. More specifically, the platform is intended to act as a supportive tool in many steps of the journalistic workflow, from data collection and data filtering to the extraction of useful information from the collected data and the automatic synthesis of news stories. In addition to the provided services, the platform aims to deliver value-added products to the editors and journalists based on EO data, allowing them, thus, to enrich their publications and news articles. Such information is important in the news that is related to disasters such as floods and fires. EO data are freely available in large quantities. However, the main obstacle to their wide use by journalists relates to the difficulties in accessing and even processing them so as to extract additional meaningful information without expert knowledge. Through this platform, users have the possibility to retrieve and extract valuable information from EO data and, specifically, from EO images, without having prior knowledge of processing satellite images. The collected data are automatically processed and provided to the journalists as valuable information regarding the effect of a disaster on a certain area. The major market segments that EarthPress targets are: (a) local newspapers, which are interested in providing breaking news with a high level of personalization; (b) nationwide newspapers, which are more interested in producing worldwide information; and (c) ePress, which are generally more specialized and could be interested in accessing more elaborated information regarding disasters and EO data.