Role of Various Features in Identification of Rumors in the Social Network

A rapid use of the social network for communication prompts the broad and quicker dispersion of information than normal news channels. The validation of information is very challenging due to massive data on a social network. Unverified news can be a talk or phony news that harms an individual, association and severe effect on humankind. Hence, battle the talk dissemination to limit the unfriendly impacts on society. Regardless of incredible endeavors to manage this issue, analysts chiefly focused on transient dynamics of posts alongside different highlights from a user, content-based, network-based features and exhibited a moderate exactness. The time series highlights are related to an occasion that smothers the other quality highlights identified with each post. There is a degree of development in precision, so this paper centers around the significant role of various features in rumor detection in social networks. This involves the highlights from the user-based, syntactic-based, lexical and temporal features. The obtained results compared to a benchmarked real-world dataset of a Twitter network.