The Status Gradient of Trends in Social Media

TL;DR

This paper introduces a framework to analyze how users of varying activity levels adopt social media trends over time, revealing differences in early adopters across domains.

Contribution

It develops the concept of a 'status gradient' to characterize user participation dynamics during trend lifecycles in social media.

Findings

Different domains show distinct patterns of early adopters.

The status gradient reveals key differences in user engagement.

Methodology applies across multiple datasets.

Abstract

An active line of research has studied the detection and representation of trends in social media content. There is still relatively little understanding, however, of methods to characterize the early adopters of these trends: who picks up on these trends at different points in time, and what is their role in the system? We develop a framework for analyzing the population of users who participate in trending topics over the course of these topics' lifecycles. Central to our analysis is the notion of a "status gradient", describing how users of different activity levels adopt a trend at different points in time. Across multiple datasets, we find that this methodology reveals key differences in the nature of the early adopters in different domains.