## 2 Materials and Methods
### TF-IDF Feature Selection and Model Construction  
Every training model starts with a good representation of data items. For text classification in particular, feature selection is the prerequisite step necessary for such a task. Various approaches are designed around the idea of selecting a set of words that best represents the document (or a set of documents). The most common text feature selection that is known is based on the idea of term frequency. Specifically, the Term Frequency and Inverse Document Frequency (TF-IDF) method [45] has been most dominant. In this section, we discuss how we extracted the bigram features needed for training the NeoNet classifier. For this task, we used a COVID-19 news dataset that was trustworthy, and publicly available (published on Kaggle). Due to the fact that raw text presents users with inherent issues (e.g., format, encoding, and punctuation), we performed a preprocessing step to address such issues. We split the list of articles into 10-folds of 500 articles. We used one-fold to be analyzed for feature selection using the TF-IDF algorithm. Given that a TF-IDF feature can be a word or more, we calibrated the algorithm to capture features that were of exactly two words (i.e., bigram). The TF-IDF scored each feature and ranked them accordingly. When the  
Figure 1: Shows a demonstrative workflow that explains the methods and the various processes starting with a news dataset, analyzing it for bigrams using Term Frequency Inverse Document Frequency (TF-IDF), constructing a network model and training the algorithm with a model to make predictions of 5-fold tests. TF-IDF was run against the training articles, it produced 193914 bigrams were produced. The TF-IDF measure produces features of a certain confidence. In the training fold (500 articles) of the dataset that we have used 193914 bigrams. Clearly, this caused the model to be noisy which also could have led to an overfitting problem. Therefore, we only selected the top 500 ranked and ignored the rest. Table 1 shows a sample of the top-ranked features selected from the training dataset before the noise removal. Figure 2 is a bar plot that also shed more insight on the ranking and the analysis of the top-40 bigrams. There are four subfigures. The top-left corner contains the top-10 which includes the following bigrams (covid 19, coronavirus outbreak, public health, health care, New York). The most significant and highly ranked term according to the dataset was \"covid 19\". Its corresponding sore according to TF-IDF was 20.9. The rest of the terms fluctuated between 3 and 7, which shows their relevance when compared with the score of the \"covid 19\" bigram. The remaining bigrams in each of the remaining three subplots contained bigrams such as \"tested positive\", \"physical distancing\", \"coronavirus cases\", \"social distancing\", and \"federal governments\". Clearly, such bigrams depict an accurate picture of the pandemic in terms of mitigation presented by \"social distancing\" and \"physical distancing\". The global impact of the pandemic was also presented by bigrams such as \"federal governments\", \"health organizations\", and \"public health\" concerns.