Structure and dynamics of growing networks of Reddit threads

TL;DR

This paper models Reddit threads as evolving complex networks, revealing unique structural properties and growth dynamics influenced by user interactions, disagreement, and community guidelines, contrasting with other social networks.

Contribution

It introduces a novel network model of Reddit threads, analyzing their structural evolution and highlighting how community rules shape interaction patterns.

Findings

Reddit networks have very low clustering coefficients.

Average shortest path length increases over time.

Two subgraphs grow at different speeds, influenced by community guidelines.

Abstract

Millions of people use online social networks to reinforce their sense of belonging, for example by giving and asking for feedback as a form of social validation and self-recognition. It is common to observe disagreement among people beliefs and points of view when expressing this feedback. Modeling and analyzing such interactions is crucial to understand social phenomena that happen when people face different opinions while expressing and discussing their values. In this work, we study a Reddit community in which people participate to judge or be judged with respect to some behavior, as it represents a valuable source to study how users express judgments online. We model threads of this community as complex networks of user interactions growing in time, and we analyze the evolution of their structural properties. We show that the evolution of Reddit networks differ from other real social networks, despite falling in the same category. This happens because their global clustering coefficient is extremely small and the average shortest path length increases over time. Such properties reveal how users discuss in threads, i.e. with mostly one other user and often by a single message. We strengthen such result by analyzing the role that disagreement and reciprocity play in such conversations. We also show that Reddit thread's evolution over time is governed by two subgraphs growing at different speeds. We discover that, in the studied community, the difference of such speed is higher than in other communities because of the user guidelines enforcing specific user interactions. Finally, we interpret the obtained results on user behavior drawing back to Social Judgment Theory.