## 6 Conclusions  
The paper proposed a novel hybrid deep learning model for detecting COVID-19-related rumors on social media based on a long short-term memory and concatenated parallel convolutional neural networks (LSTM-PCNN). The conducted experiments used three static word embedding models, which are word2 vec, GloVe, and FastText. The experimental results showed that the proposed LSTM-PCNN model achieved the highest performance when the word2 vec skip-gram model was used, and it outperformed the other baseline models, where the obtained detection accuracy reached 86.37%. The experiments also investigated adding more dense layers to the architecture of the proposed model leads. It was found that, in most cases, this adding degraded the overall performance. Statistical analysis was conducted using the Mann-Whitney-Wilcoxon test and the Wilcoxon signed-rank test and the findings showed that adding more \"Dense layers\" did not improve the performance of the proposed model. As the rumors have negative impact on the social and political aspects of many countries, the proposed model can help the health and other governmental authorities to automatically detect fake information about COVID-19 on social media and mitigate this impact. In future work, other datasets with Arabic tweets could be used, and other deep learning-based methods could be proposed and investigated to enhance the detection of health-related rumors in Arabic and other languages. Conceptualization, M.A.-S., A.A. and F.S. ; methodology, M.A.-S., A.A. and F.S. ; Software, A.A. and M.A.-S.; validation, W.B. and O.A. ; formal analysis, F.S., M.A.-S., W.B. and O.A. ; investigation, M.A.-S., F.S. and W.B. ; data curation, M.A.-S. and F.S. ; writing--original draft, M.A.-S.; writing--review & editing, F.S., W.B., O.A. and A.A.; visualization, M.A.-S.; supervision, M.A.-S.; project administration, M.A.-S. and F.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the Deanship of Scientific Research at Taibah University, Saudi Arabia, project number (CSE--4). The authors would like to thank the Deanship of Scientific Research at Taibah University, Saudi Arabia, for funding this research project number (CSE--4). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Embedding Layers** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline Word2 Vec (CBOW) & 84.79\% & 0.84762 & 0.84786 & 0.84760 \\ Word2 Vec (Skip-gram) & 84.79\% & 0.84971 & 0.84786 & 0.84775 \\ Fasttext (CBOW) & 85.26\% & 0.85560 & 0.85261 & 0.85294 \\ Fasttext (Skip-gram) & 86.05\% & 0.86406 & 0.86054 & 0.86084 \\ GloVe & 85.10\% & 0.85553 & 0.85103 & 0.85136 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The Median Values Obtained using Three Dense Layers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Embedding Layers** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline Word2 Vec (CBOW) & 85.10\% & 0.85093 & 0.85103 & 0.85097 \\ Word2 Vec (Skip-gram) & 85.74\% & 0.86054 & 0.85737 & 0.85767 \\ Fast Text (CBOW) & 85.42\% & 0.85577 & 0.85420 & 0.85388 \\ Fast Text (Skip-gram) & 83.99\% & 0.84059 & 0.83994 & 0.83891 \\ GloVe & 85.42\% & 0.85584 & 0.85420 & 0.85449 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The Median Values Obtained using One Dense Layer. * [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] Using Deep Learning to Detect Rumors in Twitter. In _Proceedings of the International Conference on Human-Computer Interaction, Copenhagen, Denmark, 19-24 July 2020_; Springer: Cham, Switzerland, 2020; pp. 321-334. * [PERSON] and [PERSON] (2021) [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] FakeBERT: Fake news detection in social media with a BERT-based deep learning approach. _Multimed. Tools Appl._**2021**, _80_, 11765-11788. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Arcov-19: The first Arabic covid-19 twitter dataset with propagation networks. _arXiv_**2020**, arXiv:2004.05861. * [PERSON] et al.