Relational patterns in cross-media information diffusion networks

Abstract

This paper describes an approach for identifying patterns of information diffusion across different media types on the web. In this context, a novel sampling and crawling strategy for social media content has been applied to extract contributions relevant to certain news events. Contributions can be any kind of published content in different information channels, including tweets (Twitter), web pages, or revisions of Wikipedia articles. Contributions can be interlinked by hyperlinks, revision links, or retweet relationships, and thus constitute a diffusion network with unidirectional links indicating influence. Our approach reduces the original possibly very large and complex diffusion network to its basic underlying structure by applying non-negative matrix factorization to group the nodes with similar positions in the diffusion network. Beyond focusing only on the spread of a news item through different channels, also the temporal aspect, especially delay, is explicitly taken into account.