Detection of Trending Topic Communities: Bridging Content Creators and Distributors

The rise of a trending topic on Twitter leads to the temporal emergence of a set of users currently interested in that topic. Given the temporary nature of the links between these users (more users as well as more interactions among them appear while the topic evolves), being able to dynamically identify communities of users related to this trending topic would allow for a rapid spread of information. In this paper, we tackle this challenge, by identifying coherent topic-dependent user groups, linking those who generate the content (creators) and those who spread this content, e.g., by retweeting/reposting it (distributors). This is a novel problem on group-to-group interactions in the context of recommender systems and user modeling. Analysis on real-world Twitter data compare our proposal with a baseline approach that considers the retweeting activity, and validate it with standard metrics. Results show the effectiveness of our approach to identify communities interested in a topic where each includes content creators and content distributors, facilitating users’ interactions and the spread of new information.