Detection of Trending Topic Communities: Bridging Content Creators and Distributors

TL;DR

This paper presents a novel method for dynamically detecting user communities related to trending topics on social media, linking content creators and distributors to enhance information spread.

Contribution

It introduces a new approach for identifying coherent, topic-dependent user groups that include both content creators and distributors, improving community detection in social media.

Findings

Effective identification of topic-related communities on Twitter

Outperforms baseline retweet-based approaches

Facilitates rapid information dissemination

Abstract

The rise of a trending topic on Twitter or Facebook 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, being able to dynamically identify communities of users related to this trending topic would allow for a rapid spread of information. Indeed, individual users inside a community might receive recommendations of content generated by the other users, or the community as a whole could receive group recommendations, with new content related to that trending topic. 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. 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.