Diffusion on Social Media Platforms: A Point Process Model for Interaction among Similar Content
成果类型：
Article
署名作者：
Yoo, Eunae; Gu, Bin; Rabinovich, Elliot
署名单位：
University of Tennessee System; University of Tennessee Knoxville; Arizona State University; Arizona State University-Tempe; Arizona State University; Arizona State University-Tempe
刊物名称：
JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
ISSN/ISSBN：
0742-1222
DOI：
10.1080/07421222.2019.1661096
发表日期：
2019
页码：
1105-1141
关键词：
Information diffusion
Network structure
hawkes processes
attention
propagation
COMPETITION
contagions
community
IMPACT
SPREAD
摘要：
Social media platforms disseminate a massive volume of user-generated content, some of which convey similar and overlapping information. We study how the diffusion of a given piece of content (called a cascade) is influenced by the diffusion of other cascades carrying similar content (called parallel cascades). We theorize that the diffusion of a cascade can be inhibited or amplified by that of parallel cascades containing similar content. To study this phenomenon, we formulate a generalized version of the self-exciting point process model and showcase a novel approach to evaluating the parallel diffusion of similar social media content. We estimate the model using Twitter data. We observe that, on average, the diffusion of a cascade is inhibited by the concurrent diffusion of parallel cascades with similar content. We further identify an asymmetry among content producers as the diffusion of content contributed by those with larger networks is more likely to be amplified by the diffusion of similar content. Our study underscores the importance of accounting for content similarity as failing to do so may overestimate assessments of a cascade?s diffusion. Our results also suggest that smaller, individual social media content contributors should avoid publishing repetitive content and channel their efforts towards developing novel content, while this is not a concern for larger content contributors.