## 3 Experiments and Results  
Using the training model resulting from the bigram feature selection step, we conducted a series of classification (testing) experiments. Using five different folds of the dataset, and different configurations of the minimum support parameter, we measured the precision of the NeoNet algorithm. The rationale behind this was to come up with a threshold that produced the best outcome. The minimum support parameter was based on the number of bigrams produced by each article. The higher the number of the bigrams matching, the higher the chances of an article being classified as a positive COVID-19 class. However, the experimentation guided the algorithm to identify a reasonable threshold. A very high number of bigrams would lead to classifying articles that were extremely similar in content. As a result, the algorithm would miss articles that belonged to the COVID-19 class, but less similar to the training model. On the other hand, a very low threshold would lead to classifying any article with a slight overlap as positive and would have made the algorithm not precise. We used the following min-sup configurations [5; 10; 15; 20; 25] bigrams. Figure 5 shows the five different test sets and how they were classified according to NeoNet, with various minimum support levels. The analysis shows that mandating that at least 5 bigrams were matched against the training set and produced a precision value between 99.6%-100%. If it was set to a more demanding parameter (mini-sup = 25) the classification results fluctuate between 91.18%-95.59%. The experimentations showed that somewhere in the middle is reasonable (min-sup = 15), and it produced a classification precision that fluctuated between 97.99%-99.20%. Each fold was plotted using five different curves, one for each minimum support value. We have shown above how NeoNet could be controlled using the min support to make it flexible to use in various case scenarios. However, the algorithm also performs exceptionally well without such configurations when needed. In this section we show its performance analysis compared with the most prominent algorithms (e.g., SVM, neural nets, random forests). Given the fact that such algorithms do not necessarily utilize a similar notion of the min support/confidence, we set both parameters to min-sup = 15. The algorithm was trained using a 500 articles fold. The rest of the dataset was split into 5-folds, each of 500 articles. As expected, each fold of the dataset was tested against NeoNet and compared with a counterpart algorithm. The algorithm was tested on each of the five folds and compared against all other algorithms to measure the precision achieved for all of the 5-folds. Figure 6 below shows how NeoNet's performance (shown on the far left of the x-axis) outperformed all other algorithms, unless a perfect classification results were achieved by both algorithms (e.g., NeoNet vs. Neural Net). The diagram shows a common theme: Except for NeoNet, Fold-3 (depicted in grey) appeared to be scored the lowest among all the algorithms. Another noteworthy observation is that Fold-1 and Fold-5 also appeared to be scored the highest (99.2%) among several algorithms which include Stochastic Gradient Descend, SVM, Random Forests, and Neural Nets. This was as close as it got when their precision was compared with NeoNet. However, when all of the algorithms failed to achieve a perfect precision, NeoNet showed dominance and outperformed all algorithms. We conducted the experiment using the Orange Data Mining Toolbox in Python [59]. Figure 5: Shows the performance of NeoNet with different configurations of the min-sup parameter against CBC News Dataset using min-sup levels: [5; 10; 15; 20; 25]. A reasonable explanation for the outstanding performance that NeoNet demonstrated is the feature selection part when training the algorithm. Clearly, the TF-IDF bigram features were more indicative than counterpart unigrams with mere frequency.