## 2 Related Studies
### Rumor Detection Approaches  
During the outbreak of COVID-19, especially when countries around the world began implementing a ban and a full lockdown, a wave of panic spread rapidly among citizens. Due to this, the WHO emphasized the need to fight against misinformation related to the virus and the methods of treatment [12]. To achieve this, the health authorities paid attention to debunking such rumors. However, verifying all these rumors required much human effort. Some researchers suggested developing AI techniques to fight against COVID-19 related misinformation/rumors on social media. Below are some of the notable works that used this approach. [PERSON] [13] embedded the pre-trained model of BERT with TextCNN and TextRNN models. The proposed model was trained on data with 3737 rumors collected from different Chinese platforms. The results showed that the proposed BERT model outperformed the other methods. In addition, all three models showed good results and could be used to defeat the COVID-19 related rumors. [PERSON] et al. [14] conducted an extensive experiment on a dataset of COVID-19 misinformation written in Arabic spread on Twitter. The n-gram TF-IDF, word level TF-IDF feature representation, word2 vec, and FastText word embedding were employed with several traditional ML and DL methods. As traditional ML methods, the random forest classifier, XGB, naive Bayes, SGD, and SVM were investigated. In addition, the CNN, bi-LSTM, and CRNN models were used as DL methods. The findings showed that the TF-IDF word level performed well when employed with traditional ML methods comparing with n-gram TF-IDF. The FastText produced better results with ML methods and the CNN. The word2 vec produced some improvement with CNN before optimizing the AUC score, while the RNN benefits more after optimizing the AUC. In [15] [PERSON] et al. suggested combining text content [16], propagation patterns [17], and user feedback. They also analyzed the influence of these combinations on a deep attention model. The proposed model was tested on a set of publicly available datasets. They reported that when they tried to re-obtain the contents of some tweets, about of fifteen percent Twitter data has been lost. The results showed that this approach slightly improved rumor detection in the propagation cycle and achieved a good result with 94.2% accuracy. In [18], [PERSON] and [PERSON] collected around one million Arabic tweets related to COVID-19. Their aim was not only to detect rumors, but also to identify topics discussed during the period and to find the source of such rumors. For conducting rumor detection, the authors sampled only 2000 tweets and labeled them manually. After that, SVM, LR, and NB classifiers were used to distinguish rumor tweets from non-rumors. The highest achieved accuracy was 84.03%, which was achieved by LR with count vector and SVM with TF-IDF. They also examined the influence of word2 vec and FastText on the classifiers' performance. They reported that applying the word embedding approaches did not impact positively on the classifiers' performance. Apart from Coronavirus related rumors, a large amount of studies can be found in literature that addressed rumor detection via social media in general such as [3; 5]. The subsection below briefly presents some DL techniques that are intensively used for detecting rumors via OSN. A summary of the main existing methods on using machine learning and deep learning methods for detecting rumors is shown in Table 1. The proposed model extends the existing methods in the literature by proposing a new LSTM-PCNN architecture and conducting extensive experiments to demonstrate the performance of the proposed model. In addition,this study investigated the impact of word embedding layers to select the appropriate scheme such as the influence of static word embeddings (word2 vec, GloVe, and FastText) on the proposed model. As a result, the proposed model provided interesting results and outperformed the other investigated models in terms of accuracy, recall, precision, and F-score.