# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 3 Methodology  
This section explains the methodology adopted to develop a classification model for detecting fake news on Twitter. This work seeks to find suitable detection model configurations in the context of the fake news issues by performing experiments with various word embeddings and deep learning algorithms. Figure 1 depicts the detailed phases of the proposed methodology. First, we collected data from the Twitter platform using Twitter API and then performed preprocessing, including data cleaning and normalization techniques on the collected Tweets. The next step is transforming the Tweet's text into a \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Classification**} & \multicolumn{3}{c}{**Feature Type**} & \multirow{2}{*}{**Textual Feature**} \\  & & & & **Approach** & & **News** & **Social** & **Representations** & **Result** \\ \hline
[11] & 2020 & 4547 news & General & LSTM, mBERT & ✓ & & & Word-level & \\  & & & & LSTM, mBERT & ✓ & & embeddings, & F1-score & 0.643 \\ \hline
[12] & 2020 & \
\begin{tabular}{c} AraNews \\ (97,310 news), ATB \\ (48,655 news), ANS \\ (4547 news) \\ \end{tabular} & General &
\begin{tabular}{c} mBERT, AraBERT, \\ XLM-RBase, \\ XLM-RLarg \\ \end{tabular} & & & & & & mBERT, AraBERT, & \\  & & & & XLM-RBase, & & XLM-RBase, & 0.70 \\ \hline
[15] & 2020 & 6895 news articles & Political & NB, XGBoost, CNN & ✓ & & BOW, TF-IDF, & F1-score \\  & & & & & & fastText & 0.984 \\ \hline
[1] & 2021 & 1862 Tweets & \begin{tabular}{c} Syrian \\ crisis \\ \end{tabular} & \
\begin{tabular}{c} KNN, DT, NB, LR, \\ LDA, SVM, RF, \\ XGboost \\ \end{tabular} & ✓ & ✓ & TF, TF-IDF, BoW &
\begin{tabular}{c} Accuracy \\ 0.82 \\ \end{tabular} \\ \hline
[16] & 2021 & 37,000 Tweets & \begin{tabular}{c} COVID- \\ 19 \\ \end{tabular} & \
\begin{tabular}{c} NB, LR, SVM, \\ MLP, RF, XGB \\ \end{tabular} & ✓ & & BOW, TF-IDF &
\begin{tabular}{c} F1-score \\ 0.933 \\ \end{tabular} \\ \hline
[6] & 2021 & 10,828 Tweets & \
\begin{tabular}{c} COVID- \\ 19 \\ \end{tabular} &
\begin{tabular}{c} AraBERT, mBERT, \\ distilBERT-multi, \\ mBERT COV19 \\ \end{tabular} & ✓ & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline
[14] & 2021 & 8786 Tweets & \
\begin{tabular}{c} COVID- \\ (97,0959 Tweets), \\ ArCOV19-Rumors \\ AraNews (108,194 news) \\ \end{tabular} &
\begin{tabular}{c} CNN, RNN, GRU, \\ AraBERT v1, \\ AraBERT v2, \\ PABERT v02, \\ QARiB, Ar-Electra, \\ Marbert, Arbert \\ \end{tabular} & & & & & & & & \\ \hline
[3] & 2021 & 8786 Tweets & \
\begin{tabular}{c} COVID- \\ 19 \\ \end{tabular} &
\begin{tabular}{c} XGB, RF, NB, SVM, \\ SGD, CNN, RNN, \\ CRNN \\ \end{tabular} & ✓ & & & & TF-IDF, word2 vec, & F1-score \\  & & & & & & & fastText & 0.54 \\ \hline
[17] & 2022 & 3157 Tweets & \begin{tabular}{c} COVID- \\ 19 \\ \end{tabular} & \
\begin{tabular}{c} LR, KNN, CART, \\ SVM, NB, RF, \\ AdaBoost, \\ Bagging, ExtraTree \\ \end{tabular} & ✓ & ✓ & TF-IDF, glove &
\begin{tabular}{c} F1-score \\ 0.935 \\ \end{tabular} \\ \hline
[18] & 2022 & 4299 Tweets & \
\begin{tabular}{c} COVID- \\ 19 \\ \end{tabular} &
\begin{tabular}{c} RF, DT, XGBoost, \\ SVM, KNN, NB, \\ SGD, LR, RNN, \\ BiRNN, GRU, \\ BiGRU, LSTM, \\ BiLSTM \\ \end{tabular} & ✓ & & & & & \\ \hline
[13] & 2022 & 1098 news articles & Hajj & SVM, RF, NB & ✓ & & & F1-score \\  & & & & & & & 0.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. representative multidimensional vector by applying one of the textual feature extraction methods. Textual feature extraction methods are divided into classic and contextualized methods. From classic methods, we investigated the Keras embedding layer and two pre-trained embedding models, which are word2 vec and fastText. We also considered ARBERT and MARBERT as contextualized feature extraction methods. Then, either CNN or BiLSTM is used to learn embedded vectors and extract deep semantic features.