## 3 Materials and Methods
### Breaking News Discovery  
Following the setup that was created in Section 3.1, the next step is the utilization of available data that are stored in the medium of our choice. The target is to infer breaking news from the total list of available events. It was decided that a reliable approach would consist of both NLP techniques and expert knowledge; thus, the designed component combines standard computational methods with empirical rules. Long-time journalists and reporters were engaged to formulate the breaking news detection procedure. Table 1 provides a questionnaire that was initially formed in technical terms (i.e., concerning system implementation) and was further elaborated by incorporating the feedback given by experienced newsroom professionals. While the utmost target is to address these queries to a significant number of media professionals and citizen journalists, an initial validation was obtained by ten (10) targeted users/professionals, different from those involved in the initial formulation process. The intention was to proceed with an initial parameterization and setup of the proposed system that could be further configured and fine-tuned, upon receiving more audience feedback and testing experience. The prototype algorithm that was implemented is presented in Algorithm 1. The core of the algorithm is the measurement of the similarity [50; 51; 52] of the event title under test with the rest of the titles that have appeared in the near past. The main advantage of this technique is that it does not require any prior knowledge of the processed text, as it uses a model that has already been trained on a set of documents that serves as a knowledge base. A big data set (500k words) in the Greek language is obtained and then projected to an applicable vector space [53]. Each word in the corpus is represented by a 300-dimensional vector. The vectors are calculated using the standard GloVe algorithm [54], which is a global log-bilinear regression model that combines global matrix factorization and local context window methods. This algorithm is widely used as it performs quite well on similarity tasks. The extracted vectors that are also called word embeddings contain not only the meaning of the word as an arithmetic value but its relation to other words as well. The described process is unsupervised, capable of learning by just analyzing large amounts of texts without user intervention. Moreover, it is possible to use transfer learning--a practice in machine learning where stored knowledge gained while solving one problem can be applied to a different but related problem--on an existing trained data set using a lexicon of general interest to extend its functionality, to achieve a better-contextualized word representation in specific scenarios, where different buzzwords/lexicons are used (e.g., a journalistic dictionary). Each title is also represented by a vector, which is calculated by averaging the vectors of the words that constitute it. To measure the relevance between two titles, the cosine similarity between the corresponding vectors is computed [36; 39; 41; 42]. \[S_{c}(A,B)=\frac{A\ \times B}{||A||\ ||B||} \tag{1}\]  
Cosine similarity of zero means that two given sentences have no relevance, while a value of one implies an absolute match. Various experiments were conducted to define the minimum value that implies high contextual relevance. The results of these experiments were given to the field experts that were mentioned earlier, and they set the minimum threshold to the value of 0.85.