# Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models
## 3 Arabic Dialect Identification Model
### COVID-19 Twitter Data Results  
The focus in the dataset we utilized which is described in [7] was on hashtags used in Saudi Arabia, although they might be used in Arabic-speaking countries outside of Saudi Arabia. Saudi Arabia is one of the countries with the highest amount of Twitter users among the country's online population [40,41]. Saudi Arabia also generates 40 percent of \begin{table}
\begin{tabular}{l l l l l l} \hline
**Dropout Rate** & **Batch Size** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline
0.0 & 16 & 0.8198 & 0.7611 & 0.7872 & 0.8424 \\
0.3 & 16 & 0.8166 & 0.7679 & 0.7882 & 0.8391 \\
0.5 & 16 & 0.8204 & 0.7638 & 0.7889 & 0.8429 \\
0.8 & 16 & 0.8173 & 0.7584 & 0.7844 & 0.8396 \\
0.5 & 24 & 0.8454 & 0.8026 & 0.8177 & 0.8578 \\
0.5 & 32 & 0.8504 & 0.7946 & 0.8156 & 0.8588 \\
0.5 & 48 & 0.8423 & 0.7938 & 0.8053 & 0.8503 \\
0.5 & 64 & 0.8338 & 0.8020 & 0.8066 & 0.8500 \\
0.5 & 84 & 0.8511 & 0.8014 & 0.8203 & 0.8617 \\ \hline \end{tabular}
\end{table} 
Table 4: The 4 classes dialect identification results of our best proposed model for a wide range of dropout rate and batch size values. Figure 5: Analysis of MarBERT-based model results. (**Left**): The confusion matrix for 3-dialects classification. (**Right**): The confusion matrix for 4-dialects classification. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Embeddings Level** & **Embeddings Type** & **Model** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline Word (unigrams) & TFIDF & Logistic Regression & 0.87 & 0.83 & 0.85 & 0.853 \\ Word (1k unigrams) & TFIDF & Logistic Regression & 0.86 & 0.82 & 0.84 & 0.8423 \\ Word (5k unigrams) & TFIDF & Logistic Regression & 0.87 & 0.83 & 0.85 & 0.8553 \\ Word (10k unigrams) & TFIDF & Logistic Regression & 0.88 & 0.84 & 0.85 & 0.8568 \\ Word (unigrams) & TFIDF & Logistic Regression & 0.87 & 0.83 & 0.85 & 0.8548 \\ Word (unigrams, bigrams, trigrams) & TFIDF & Logistic Regression & 0.83 & 0.82 & 0.83 & 0.8315 \\ Word (unigrams) & Random Embeddings & BiLSTM & 0.8703 & 0.8531 & 0.8605 & 0.8659 \\ Word (unigrams) & AOC Embeddings & BiLSTM & 0.8522 & 0.8393 & 0.8449 & 0.8513 \\ Word (unigrams) & Twitter Embeddings & BiLSTM & 0.8742 & 0.8669 & 0.8703 & 0.876 \\ Word (unigrams) & Random Embeddings & Attention BiLSTM & 0.8568 & 0.8507 & 0.8533 & 0.8577 \\ Word (unigrams) & AOC Embeddings & Attention BiLSTM & 0.847 & 0.8375 & 0.8418 & 0.8473 \\ Word (unigrams) & Twitter Embeddings & Attention BiLSTM & 0.8792 & 0.8697 & 0.874 & 0.8793 \\ Word (unigrams) & Random Embeddings & Self Attention & 0.8325 & 0.8147 & 0.8221 & 0.8294 \\ Word (unigrams) & Twitter Embeddings & Self Attention & 0.8467 & 0.8221 & 0.832 & 0.8423 \\ Word (unigrams) & AOC Embeddings & Self Attention & 0.824 & 0.7935 & 0.8048 & 0.8158 \\ Word (unigrams) & Random Embeddings & Transformer Encoder & 0.8619 & 0.8536 & 0.8574 & 0.8585 \\ Word (unigrams) & Twitter Embeddings & Transformer Encoder & 0.8563 & 0.8462 & 0.8507 & 0.8573 \\ Word (unigrams) & AOC Embeddings & Transformer Encoder & 0.803 & 0.7753 & 0.7857 & 0.7995 \\ Subword & AraBERT Embeddings & AraBERT & 0.8802 & 0.8613 & 0.8692 & 0.8749 \\ Subword & ArabicBERT Embeddings & ArabicBERT & 0.8852 & 0.8654 & 0.8739 & 0.8739 \\ Subword & MARBERT Embeddings & MARBERT & 0.9039 & 0.8993 & 0.9011 & 0.9045 \\ \hline \end{tabular}
\end{table} 
Table 3: The 3 classes dialect identification results. all tweets all over Arab countries [42]. The used dataset has several sections including conversations discussing the precautionary measures governments have applied, conversations discussing social solidarity, and conversations supporting the governments' decisions. Moreover, the dataset has data from three Saudi official accounts. The amount of tweets in the dataset is around 3.8 million. We applied the BERT-based dialect recognition model on this dataset and selected the tweets that achieved a 90% confidence score (based on the SoftMax score), which provided 1.76 million tweets. We randomly selected 2500 tweets from each region set and made a manual review for them.