Online social networks are very popular among people, and they are changing the way people communicate, work, and play, mostly for the better. One of the things that fascinates us most about social network sites is the resharing mechanism that has the potential to spread information to millions of users in a matter of few hours or days. For instance, a user can share the content (e.g., videos on YouTube, tweets on Twitter, and photos on Flickr) with her set of friends, who subsequently can potentially reshare the content, resulting in the development of a cascade of resharing. Such information cascades play a significant role in almost every social network phenomenon, which include, but are not limited to, the diffusion of innovation, persuasion campaigns, and spreading rumors. Information cascade prediction is to infer some key properties of information cascades, such as their sizes and shapes, which indicate the extent to which the information can reach in the social network. This prediction task can be valuable, and it can be applied in an array of areas, such as content recommender systems and monitoring the consensus opinion. However,
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.