# Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
## 7 Related Work  
Rumor detection on social media is an existing problem in the literature. Many researchers have experimented to find a good solution to this problem. In [38], the authors developed a two-layer network to model the interaction between epidemic spreading and information diffusion. Their results showed that knowledge diffusion can eradicate both rumors and epidemics, where the penetration intensity of knowledge into rumor plays a crucial role. It increases the thresholds for rumors and epidemics to break out. According to [39], a complex network can be modeled as a graph, which usually consists of nodes and edges. Identifying the most influential node in a complex network has real world applicability, for instance, rumor spreading in social networks. The authors presented a survey on the identification of influential spreaders in complex networks by analyzing and comparing major variations of k-shell based methods along with representative network topology based hybrid techniques. The coreness of the nodes is considered in a typical k-shell method by dividing the network into shells. In [40], the authors proposed a distributed computing approach to calculate the network centrality value for each user in a social network. They used the MapReduce approach in the Hadoop platform, which allows better computational performance than that of conventional implementation. Their results showed that with the distributed approach they improved the calculation performance of degree centrality, closeness centrality, and eigenvalue centrality on average by significant margins over the conventional approach. The authors of [41], proposed a hybrid recommendation model to improve recommendation accuracy. Their model is based on users' ratings, reviews, and social data. By conducting a variety of experiments their results showed that their proposed model helps improve the recommendation accuracy. In [42], authors have explored user-specific features along with content characteristics of social media messages and proposed an information propagation model based on heterogeneous user representation to observe distinctions in the propagation patterns of rumors and credible messages and using it to differentiate them, and their study identifies that rumors are more likely to spread among certain user groups. To predict a document in a social media stream to be a future rumor and stop its spread [PERSON] et al. [43] used content-based features along with novelty-based features and pseudo feedback. In [7], a sentiment dictionary and a dynamic time series algorithm based Gated Recurrent Unit model is proposed, that identifies fine-grained human emotional expressions of microblog events and the time distribution of social events to detect rumor events. By treating microblog users' behaviors as hidden clues to detect possible rumormongers or rumor posts, [PERSON] et al. [44] proposed a user behavior-based rumor identification schemes, which focuses on applying traditional user behavior-based features as well as authors' proposed new features that are extracted from users' behaviors to rumor identification task and concluded that rumor detection based on mass behaviors is better than detection based on microblogs' inherent features. In [10], temporal, structural, and linguistic features of social media rumors were explored for rumor classification task and using those features together helped in identifying rumors more accurately. [PERSON] et al. [45] proposed a graph-kernel based hybrid SVM classifier that can capture high-order (message) propagation patterns as well as semantic features, for example, the topics of the original message for automatically detecting false rumors on Sina Weibo. As discussed above, most of the works focus on medium to heavy weight feature extraction processes, which makes them slow in identifying false information on social media since the fast-pacedenvironment of social media allows a very little amount of time to analyze a piece of information before it propagates all over the network.