Mapping Social Media User Behaviors in Reciprocity Space

TL;DR

This paper introduces a reciprocity-based framework that maps social media users onto a two-dimensional space, revealing continuous behavioral gradients and unifying diverse user types within a single model.

Contribution

It presents the first unified, reciprocity-based model of social media behaviors, capturing the full spectrum of user types as regions in continuous space.

Findings

User behaviors vary smoothly across reciprocity space.

Fragmented user types are regions, not discrete categories.

The framework offers interpretable influence metrics.

Abstract

Social media users exhibit diverse behavioral patterns as platforms function simultaneously as information and friendship networks. We introduce a reciprocity-based framework mapping users onto two-dimensional space defined by bidirectional connection ratios. Analyzing 48,830 Twitter users and 149 million connections, we demonstrate that fragmented user types from prior studies (influencers, lurkers, brokers, and follow-back accounts) emerge naturally as regions within continuous behavioral space rather than discrete categories. User properties vary smoothly across the reciprocity dimensions, revealing clear behavioral gradients. This framework provides the first unified model encompassing the full spectrum of social media behaviors and offers interpretable metrics for influence measurement and platform design.