###### Abstract  
Nowadays, news coverage implies the existence of video footage and sound, from which arises the need for fast reflexes by media organizations. Social media and mobile journalists assist in fulfilling this requirement, but quick on-site presence is not always feasible. In the past few years, Unmanned Aerial Vehicles (UAVs), and specifically drones, have evolved to accessible recreational and business tools. Drones could help journalists and news organizations capture and share breaking news stories. Media corporations and individual professionals are waiting for the appropriate flight regulation and data handling framework to enable their usage to become widespread. Drone journalism services upgrade the usage of drones in day-to-day news reporting operations, offering multiple benefits. This paper proposes a system for operating an individual drone or a set of drones, aiming to mediate real-time breaking news coverage. Apart from the definition of the system requirements and the architecture design of the whole system, the current work focuses on data retrieval and the semantics preprocessing framework that will be the basis of the final implementation. The ultimate goal of this project is to implement a whole system that will utilize data retrieved from news media organizations, social media, and mobile journalists to provide alerts, geolocation inference, and flight planning. breaking news; semantic processing; natural language processing (NLP); drone journalism; events location estimation +
Footnote †: journal: _Future Internet_  
Even though it is undoubted that drones can be used in several on-field scenarios, their usage is not yet commonplace. The main reason for this is that ethics and safety concerns have led to the adoption of strict operating policies in most countries [7; 8]. In addition, problems concerning intrusiveness and aesthetic ideals have been posed by photojournalists [9]. The future relaxation of the commercial application restrictions will enable the media industry and individual reporters/professionals to consistently use them. In the United States, the New York Times and the Washington Post are already using drones to take photos and videos of simulated news events at an FAA-designated test site at Virginia Tech University [10]. They have also started preparing the new generation of \"dronalists\", i.e., journalists trained as Pilots in Command (PICs), to use this new, exciting technology in day-to-day operations. In this spirit, this work proposes a framework for future drone journalism services, which will be fully deployable as soon as regulations have been conducted to overcome the aforementioned restrictions. The usage of computational and robotic resources is becoming widespread in the ecosystem of Journalism 3.0. A variety of emerging journalistic forms (textual, audio, and pictorial) are enriching online media content [11]. Natural language generation (NLG) is changing production, as it can perform functions of professional journalism on a technical level [12], leading to automatic news generation [13], sometimes making use of Machine Learning (ML) and/or natural language processing (NLP) [14; 15; 16; 17]. ML is defined as the study of algorithms that use computer systems to perform tasks without being instructed to (i.e., learning by example), while NLP, which is a part of Artificial Intelligence (AI), deals with the analysis and processing of human natural language [18]. While multi-source news retrieval is becoming commonplace, methods are in development to verify the newsgathering and the publication process [19; 20; 21]. Dedicated search and retrieval mechanisms, implemented as software agents, can be applied in data that are stored in intermediate storage locations, inferring useful information for further analysis and monitoring [22]. Even with the absence of prior knowledge, it is possible to extract events based on semantics in text and/or speech [23] or a specific timeline of events [24].