# Machine Learning in Detecting COVID-19 Misinformation on Twitter
## 3 Methods
### Framework of Proposed Models  
It is very challenging to find the most feasible solution for detecting misinformation on social media platforms such as Twitter. Here, we identify misinformation or fake news transmitted through Twitter as tweets. We propose efficient misinformation-detection models for detecting misinformation or fake news on Twitter. In this section, the proposed models are explained. Figure 1[90] shows the general architecture that was applied on the proposed models.  
1. Data cleaning and preprocessing: they are conducted to eliminate unwanted or irrelevant data or noise in the supplied dataset in order to produce the corpus in a clean and understandable format to improve data accuracy. This step involves the removal of unwanted symbols such as punctuation, special characters, URLs, hashtags, www, HTTPS, and digits. After the data are cleaned, they are preprocessed, including stop-word removal, stemming, and lemmatization. Here, we only removed the stop words.
2. Feature extraction: After performing data cleaning and preprocessing, it is important to extract the features from the text documents. There are many features, but the most important and commonly used are words. In this step, extracted features are converted into vector representation. For this model, TF-IDF was selected for converting  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **Equation** & **Explanation** \\ \hline Accuracy (acc) & \(\frac{TP+TN}{TP+TN+FP+FN}\) & The fraction of observations that are correctly classified. \\ Precision (P) & \(\frac{TP}{TP+FP}\) & The fraction of correctly classified positive classes from the set of observations that were predicted to be positive. \\ Recall (R) & \(\frac{TP}{TP+TN}\) & The fraction of correctly classified positive classes from the set of observations that were classified correctly. \\ F measure & \(\frac{2*P*R}{P+R}\) & The harmonic mean of precision and recall metrics. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance evaluation metrics.  
text features into corresponding word-vector representation. The generated vector may be high0 dimensional.
3. Feature selection and dimensionality reduction: dimensionality reduction is important in text classification applications to improve the performance of the proposed models. It reduces the number of features to represent documents by selecting the most essential features to project the documents. Feature selection is most important in dimensionality reduction since it selects the most essential feature, capturing the essence of a document. There are various feature-selection and dimensionality-reduction models, from which singular value decomposition was implemented here, which is one of the most effective models. After dimensionality reduction, the entire corpus is divided into training and test sets.
4. Sampling the training set: Sampling is mainly performed on an imbalanced corpus to rebalance class distributions. There are mainly two types of sampling: over- and undersampling. Oversampling duplicates or generates new data in the minority class to balance the corpus, whereas undersampling delete or merges the data in the majority class. Oversampling is more effective, since undersampling may delete relevant examples from the majority class. Here, oversampling was performed to rebalance the training corpus.
5. Training: the proposed model is trained using the training corpus.
6. Performance evaluation: the performance of each model is evaluated using different evaluation metrics such as accuracy, precision, recall, F measure, and PR-AUC.
7. Hyperparameter optimization: Hyperparameters are very significant since they directly impact the characteristics of the proposed model, and can even control the performance of the model to be trained. So, for improving the effectiveness of the proposed model, hyperparameters are tuned.