## 2 Related Work  
Drones are already being used in video capturing and news coverage. Research has been carried out for flight planning and approaching inaccessible locations [34; 35]. Such topics are out of the context of this work, as they refer to problems that will be faced in the last chain link of the complete system. The current stage of implementation focuses on data preprocessing and more specifically on breaking news detection; thus, the literature that is presented below refers to such semantic analysis. One of the novelties and research contributions of the current work lies in the implemented solutions for the specific problem of multi-source breaking news detection, which makes a strong proof of concept of the proposed scenario. Based on the conducted literature/state-of-research review (and to the best of our knowledge), it seems that previous works dealing with the same problem and under similar constraints are very limited and/or entirely missing. The majority of similar approaches process social media messages (mostly tweets) [36; 37; 38; 39], and only a few of them exploit website metadata (e.g., HTML tags [40]) or use the whole article text [41] to detect hot words. Most of the recent works seem to deal with hot topic detection, which can also be adapted to breaking news detection. However, these are two different problems in principle, i.e., \"breaking news\" impliesurgency, while \"hot topic\" implies popularity. Overall, the current work proposes a holistic treatment incorporating the detection of breaking news from unstructured data emanated by multiple sources (therefore including potential UGC contributions). The implicated spatiotemporal and contextual awareness perspectives model a semantic processing framework with modular architecture and significant future extensions on drone journalism news covering automations. A common technique for detecting hot topics is the use of keywords [36; 41]. Such techniques usually show low precision, as they are based on keyword occurrence frequency in documents written by the general public [36]. The careful selection of keywords can lead to reliable results. The collection of keywords by social media posts can be very efficient. If this collection is extracted by selected reliable users, taking into consideration their influence and expertise can further improve this method [36]. [PERSON] et al. [36] proposed the use of a modified version of TF-IDF that incorporates a temporal factor to be able to tackle the difficulty of detecting near-future breaking news. Natural Language Inference (NLI) includes methods for predicting whether the meaning of a piece of text can be inferred by another [42]. A form of NLI is paraphrasing, which is also called text pair comparison. Computing the similarity among pieces of text is used in various applications, and it can be applied in this use case as well. Jensen-Shannon divergence is used to measure the similarity between two topics, which is used in topic tracking [43]. An interesting approach is the TDT_CC algorithm--Hot Topic Detection based on Chain of Causes [37]. This algorithm treats events as topic attributes and uses them to update a graph, aiming to detect a trend in a large graph in real time. Traditional algorithms dedicate too much time to traversing a graph, while TDT_CC tackles this issue by focusing on the structural topology. Graphs are also used to effectively compute the relevance between textual excerpts [38]. [PERSON] et al. [38] utilized a term graph to measure the relevance between a tweet and a given breaking event. Clustering is used in all of the above methods at some stage. [PERSON] et al. [39] applied clustering to filtered, collected tweets and then scored the clusters based on the number of tweets, which led them to breaking news detection. This is a simple and reliable technique which can only be applied on social media. In addition, this technique is not efficient in terms of memory usage and execution time, which makes it quite unusable in real-world deployments.