## 4 Results
### A 3-Steps Validation of the Word2 vec Embeddings  
The train_word2 vec function of the wordVectors R package was used to obtain the model (W2V) once the data had been generated. The following settings were used: \"vectors = 200, threads = 4, window = 12, iter = 5, negative_samples = 0\". These parameters have been applied by the _wordVectors_ authors in related research [63]. We performed a three-step validation of W2V as in previous related research [66]. We utilized a subset of the original [PERSON] article analogies [12] for the first one. In NLP, the task of finding a word analogy is represented as \"a is to b as c is to ____.\"  
The classic Mikolov example is: king is to man as woman is to ____.' --also represented as king - man + woman =? The human brain can recognize that the answer is the word 'queen'. However, for a machine to understand this pattern and fill in the blank with the most appropriate word requires a lot of training using a huge corpus (for example, the whole of Wikipedia; in our case, we are using only the obtained 374,421 titles from r/Coronavirus). Using our obtained model (namely W2V), the example analogy is represented as: W2V(\"king\") - W2V(\"man\") + W2V(\"woman\") =? We obtained promising results (as presented in Table 2) for several analogies from previous research [66], for example:  
Analogy: brother - sister + husband =? Answer: wife (0.5985)  
The number in brackets is the cosine distance between the vector embedding for the term 'wife' and the vector that is the result of the operations on the left-hand side of the equation. As the second step of W2V validation, from a representative list of specific terms related to COVID-19, we run our W2V model on each of them (for example, the term \"anosmia\") to identify its three closest terms using the following command:  
nearest_to(W2V[\"anosmia\"],3) =? As a result, we obtained the following set of the three closest terms to \"anosmia\":  
{olfactory (0.463); parkinson (0.459); aspirin (0.496)}  
In Table 3, we present the closest terms retrieved by our model and their cosine distances to several COVID-19 representative terms of a known COVID-19 glossary [67]. We proceeded through the closest terms and identified related publications and evidence supporting them, noting the high relevance of all the discovered terms in order to demonstrate the capacity of our W2V model to uncover relevant related terms (Table 3). \begin{table}
\begin{tabular}{c c} \hline
**Category** & **Closest Terms (Cosine Distance)** \\ \hline paris – france + italy =? & rome (0.584), milan (0.510) \\ \hline brother – sister + husband =? & wife (0.598) \\ \hline dad – mom + father =? & mother (0.546), family (0.569) \\ \hline she – he + girl =? & boy (0.375) \\ \hline his – her + boy =? & girl (0.570), schoolgirl (0.604) \\ \hline she – he + mother =? & father (0.373), husband (0.403) \\ \hline boy – girl + man =? & woman (0.553) \\ \hline doctor – hospital + teacher =? & school (0.577), teen (0.548) \\ \hline cnn – news + netflix =? & film (0.640), movies (0.692) \\ \hline iphone – apple + android =? & isos(0.406), tablet (0.4760), app (0.487) \\ \hline moscow – putin + nyc & [PERSON] * (0.619), Brooklyn (0.581) \\ \hline young – teen + old & 64 (0.633), aged (0.563) \\ \hline \end{tabular}
\end{table} 
Table 2: A subset of analogies from previous research [66] and the obtained results. In Table 3, we show the closest terms retrieved by our model and their cosine distances to representative definitions from the initial terms of the glossary (terms starting with the 'A' letter). For each of the terms identified by our trained model, we included relevant published scientific literature. For example, the first term in the glossary was \"ards\" (acute respiratory distress syndrome); our model retrieved Remestemcel, and its cosine distance was 0.364. We referenced [PERSON] et al. [68] because in their systematic review of Stem cell therapy in COVID-19, results on Remestemcel were recently reported. Similarly, for glucose, we referenced [PERSON] et al.