Uncovering news-Twitter reciprocity via interaction patterns

In recent years, the amount of information shared (both implicit and explicit) between traditional news media and social media sources like Twitter has grown at a prolific rate. Traditional news media is dependent on social media to help identify emerging developments; social media is dependent on news media to supply information in certain categories. In this paper, we present a principled framework for understanding their symbiotic relationship, with the goal of (1) understanding the type of information flow between news articles and the Twitterverse by classifying it into four states; (2) chaining similar news articles together to form story chains and extracting interaction patterns for each story chain in terms of interaction states of news articles in the story chain, and (3) identifying major interaction patterns by clustering story chains and understanding their differences by identifying main topics of interest within such clusters.