{"title":"Structure and dynamics of growing networks of Reddit threads","authors":"Diletta Goglia, Davide Vega","doi":"arxiv-2409.04085","DOIUrl":null,"url":null,"abstract":"Millions of people use online social networks to reinforce their sense of\nbelonging, for example by giving and asking for feedback as a form of social\nvalidation and self-recognition. It is common to observe disagreement among\npeople beliefs and points of view when expressing this feedback. Modeling and\nanalyzing such interactions is crucial to understand social phenomena that\nhappen when people face different opinions while expressing and discussing\ntheir values. In this work, we study a Reddit community in which people\nparticipate to judge or be judged with respect to some behavior, as it\nrepresents a valuable source to study how users express judgments online. We\nmodel threads of this community as complex networks of user interactions\ngrowing in time, and we analyze the evolution of their structural properties.\nWe show that the evolution of Reddit networks differ from other real social\nnetworks, despite falling in the same category. This happens because their\nglobal clustering coefficient is extremely small and the average shortest path\nlength increases over time. Such properties reveal how users discuss in\nthreads, i.e. with mostly one other user and often by a single message. We\nstrengthen such result by analyzing the role that disagreement and reciprocity\nplay in such conversations. We also show that Reddit thread's evolution over\ntime is governed by two subgraphs growing at different speeds. We discover\nthat, in the studied community, the difference of such speed is higher than in\nother communities because of the user guidelines enforcing specific user\ninteractions. Finally, we interpret the obtained results on user behavior\ndrawing back to Social Judgment Theory.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Social and Information Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.04085","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.