###### Abstract
Paving the way for \"dronalism\", the need for the automated deployment of drones as autonomous unmanned robotic journalists has emerged. The process of news retrieval and real-time coverage of an event can be highly automated. Our hypothesis is that today's technology is mature enough to allow the implementation of a service for automated breaking news detection and notification-based communication with a drone management subsystem. The main goal is the enhancement of journalistic workflows in news coverage. More specifically, this paper aims at launching prototype automation services in drone journalism, facilitating event detection and spatiotemporal localization processes until the flight-plan execution and the news-scene coverage completion. Prototype data monitoring and recording processes will be deployed (retrieval, normalization, processing, and warehousing) to organize, classify, and annotate content/metadata properly. Stream queries and analysis will rely on the articles/posts of news-sites and social networks (blogs, Twitter, etc. ), along with the transcription and indexing of press media, selected radio, and TV broadcasting programs. The aim is to accelerate human processing with machine automation that executes multiple/parallel time-, location-, and context-aware correlations in real time for event-alerting purposes. The validation of the extracted alerts and the exploitation of geographical/map information allows ground access areas to be found and recommended, where Unmanned Aerial Vehicles (UAVs) can take off towards a region of interest. Thereafter, flight-plan preparation and guided/semi-automated flight navigation and coverage can be further investigated, as long as specific criteria are met. These processes make a good fit for this current Special Issue. One of the key elements that had to be answered from the beginning of the project is the degree to which the potential users would find the envisioned service useful, and to investigate the associated requirements and the preferred functionalities to implement, which is aligned with the analysis phase of standard software development procedures [25; 26]. To achieve this, a survey was carefully designed and conducted to serve the needs of audience analysis. In this context, it is equally essential to implement and validate the applicability of the algorithmic breaking news detection back-end, which stands as the core element for serving the whole idea. Hence, the whole concept had to be tested both in terms of users' acceptance and technologic solutions' applicability. In this direction, NLP systems were implemented as the initial algorithmic solutions and were thoroughly evaluated at various levels to provide a convincing proof of concept of the tested scenario. The current work proposes a framework for functioning and supporting drone journalism services, incorporating the existing regulatory framework, security, and ethical matters [8; 27; 28]. In this context, the technological and functional capacities of drones, i.e., the capability to create powerful narratives, will be combined with a structured and automated way of operation, including alerting, control data transfer, and normalization services. The system is modular, able to integrate UGC and news alerts sent by mobile journalist devices, making use of the inherent localization and networking capabilities [25; 29; 30; 31; 32; 33], or even cooperative drone monitoring, thus offering time-, location- and context-aware data-driven storytelling. This will lead to the faster and more reliable detection of breaking news with their contextual and spatiotemporal attributes, using crowd computer intelligence techniques with appropriate weighting mechanisms. Modularity does not only concern the retrieval and the processing stage but the output forwarding as well. Alerts will also be sent to mobile journalists to let them cover the event too. Diverse drone footage and UGC mobile streams can be combined to offer a more holistic coverage of the events.