Platforms often host multiple online groups with overlapping topics and members. How can researchers and designers understand how related groups affect each other? Inspired by population ecology, prior research in social computing and human-computer interaction has studied related groups by correlating group size with degrees of overlap in content and membership, but has produced puzzling results: overlap is associated with competition in some contexts but with mutualism in others. We suggest that this inconsistency results from aggregating intergroup relationships into an overall environmental effect that obscures the diversity of competition and mutualism among related groups. Drawing on the framework of community ecology, we introduce a time-series method for inferring competition and mutualism. We then use this framework to inform a large-scale analysis of clusters of subreddits that all have high user overlap. We find that mutualism is more common than competition.
翻译:平台通常会容纳多个主题和成员重叠的在线小组。 研究人员和设计师如何能理解相关群体相互影响? 受人口生态学、社会计算和人类计算机互动方面的先前研究的启发,通过将群体规模与内容和成员构成的重叠程度联系起来,对相关群体进行了研究,但产生了令人费解的结果:重叠与某些情况下的竞争有关,但与另一些情况下的相互性有关。 我们建议,这种不一致的结果在于将群体间的关系集中到一个整体的环境影响中,从而模糊了相关群体之间的竞争和相互性的多样性。 借鉴社区生态框架,我们引入了一种时间序列方法来推断竞争和相互性。 我们随后利用这个框架来为大规模分析小编辑的集群提供信息,这些分类都与用户高度重叠。 我们发现,相互性比竞争更为常见。