# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 5 Conclusions and Future Work  
Combating and eliminating fake news across social networking platforms is challenging. A few studies have been performed to mitigate fake news in the Arabic language, and the outcome of those studies still needs improvement. In this study, we presented a deep learning approach to filter fake news in Arabic Tweets. To estimate the effectiveness of our proposed deep learning approach, we created a novel dataset containing news Tweets that were labelled as real or fake. We explored the efficiency of the Keras embedding layer with well-known pre-trained word embeddings (word2 vec, fastText, ARBERT, and MARBERT) using two deep learning algorithms (CNN and BiLSTM) to find an efficient model in the context of the false news detection problem. According to the findings of this study, contextual embeddings, ARBERT and MARBERT, produced the most optimal word representations, which were reflected in the model performance. Regarding deep learning algorithms, the model architecture that relied on CNN performed better than the BiLSTM for most situations. The best performing model was obtained by combining MARBERT and CNN, with high accuracy and an F1-score of 0.956. Incorporating user features with textual features was shown to aid in the performance improvement of false news detection. Compared to existing deep learning models for false news detection, the advantage of our work is that it can be applied to news on different topics. Another advantage is detecting news before its spreads and infects society and news sites. This is due to utilizing both news content and the profile of the user who posted the news. All of these features are readily available at an early stage in the propagation of news.  
Although this work achieved promising results, some limitations and challenges still need to be resolved in the future. First, the collected dataset contained few fake news Tweets compared to real news Tweets. Hence, further training on a balanced larger dataset is required to enhance the model generalization. Second, incorporating user characteristics resulted in an enhanced model performance by 3%. We intend to explore more effective user features to be able to distinguish fake news publishers from legitimate users. Additionally, this work could be improved by employing fine-tuned Arabic BERT-based models. Lastly, recent fake news Tweets often contain deceptive images and videos that look like true news to attract more attention from readers. Future work should concentrate on developing a model to recognize both fake textual and visual content.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Approach Used**} & \multicolumn{2}{c}{**Performance Metric**} \\ \cline{3-5}  & & & **Accuracy** & **F1-Score** \\ \hline \multirow{2}{*}{ArCOV19-Rumors} & [18] & BiLSTM & 0.8000 & 0.7900 \\  & Proposed & MARBERT-CNN & 0.8630 & 0.8603 \\ \hline \multirow{2}{*}{COVID-19 misinformation} & [3] & CNN-RNN & 0.7000 & 0.5400 \\  & Proposed & MARBERT-CNN & 0.8707 & 0.8377 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of the proposed model with the results obtained by previous models on publicly available datasets.