Structure and dynamics of growing networks of Reddit threads

TL;DR

This work studies 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 and model threads of this community as complex networks of user interactions growing in time, and analyzes the evolution of their structural properties.

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.

Figures (9)

• Figure 1: Statistics of the AITA threads dataset. In (a) the ECDF of the size (number of comments) per threads while in (b) distribution of final verdicts of the threads.
• Figure 2: Distribution of the disagreement (computed as a thread entropy) across all the collected threads from the AITA community. Values fall in the range [0, 2.6]. Vertical lines divide the disagreement in low ($H < 0.65$), medium-low ($.65< H < 1.3$), medium-high ($1.3< H < 1.95$) and high ($H > 1.95$).
• Figure 3: Distribution of coefficients ($\gamma$) of AITA networks' degree distribution.
• Figure 4: An example of user interaction network built from an AITA thread. Nodes (voters in red, not voters in blue) are users and direct edges $e_{ij}$ represent comments from user $i$ to user $j$. The graph shows two different sub-structures: a star, with the hub node corresponding to the original poster and everyone who replied to it, and a periphery consisting of comments and replies among users.
• Figure 5: Distribution of users expressing their opinion as a vote (left) and of users joining the thread with a first-level comment directly to the author of the post (right). On average, (i) half of participants express a vote, and (ii) 60% of users in the AITA community join the thread in the star. Conversely, the periphery includes most of the participants discussing without expressing a vote.