## 2 Related Work
### Rumor Detection  
Rumor detection has been a hot issue of embedding in recent years, and there are two approaches used to propose solutions: model based or feature based. In some papers, multiple features are proposed, i.e., content features and social context features or network features, while in some papers, the best algorithms are discussed to find out rumors. In article [10], they addressed two basic problems. The first issue is retrieving rumor-related micro blogs on the internet. The second difficulty is to find tweets that supported the rumor. They evaluated the effectiveness of three extracted features: content-based features, network-based features, and micro blog features. They used specific memory for the correct classification of rumors. They performed manually 10,000 experiments on the tweet and represented how their related model achieves more than 0.95 on the map. This shows that this data set is one of the largest data sets on rumors detection. In [11], the authors proposed a new model for the detection of rumors on twitter in 2012. In that literature work, they described how rumors spread after an earthquake disaster and discussed how we can deal with those types of rumors. They first investigated the actual instance of a rumor that was generated after a disaster, and they attempted to disclose the rumor characteristics. They designed a model system that can detect candidates of rumors from twitter based on the investigation. They extracted tweets that contain the keyword \"server room\" or geek \"how and \"come oil\" for r1 and r2 data sets, and they manually checked in order to remove irrelevant tweets to obtain 1135 tweets from r1 and 242 tweets from r2. In this research, the author used the (SPADE) [12] social spam analysis and detection framework across more than one social network to show the flexibility and efficiency of the cross down classification. They produced the results on large-scale study web pages, email spam, for an extended period. They used these basic models: first is the profile-based model, the second is the message-based model, and the last one is the web page model in SPADE. All the models represent the most critical object on the social network. The data of all models are stored in XML because of the extensibility and scalability of the models. They use the F measure and accuracy evaluation for the result. The proposed classifier improves the accuracy by 7% and FP rate above 20%. A model for automatically detecting rumors on social media networks was proposed [13]. The authors used implicit content-based and user-based features, such as popularity orientation, external and internal consistency, sentimental polarity, and the degree of match of the message. Features selection was the most critical work in this paper. Their work contained three-part data cleansing, feature extraction, and model training. In the data cleaning process, they filtered out spam messages, such as other old features. After features selection, they applied the classifier with SVM and random forest classifier. Finally, they showed their improvement in precision and recall. Correctly rumor identification and belief investigation on social media networks, such as twitter, were proposed [14] in 2016. In this literature, they attempted to solve the problem of rumor detection on the twitter platform. They extracted two new features in their proposed model: first is addressing the missing word and latency issues (TLV), and the second is user belief about each rumor. The SVM tree kernel model 5 was applied to it for the detection of rumors. To check the proof of another classifier of other classifier, including j48, NB, the proposed performance is better. In this research, the detection of rumors using the supervised machine learning technique on twitter data was proposed [15]. They used a two-fold supervised machine learning approach for the detection of rumors. They applied multiple models, such as Naive Bayes, Support Vector Machine (SVM), etc.,to achieve an accuracy of 81%.