Modeling retweeting behaviors is important for understanding and predicting how information spreads on social media platforms. The present study contributes to the literature by examining the decreasing social contagion and increasing homophily effects with the depth of diffusion cascades. To test the hypotheses, the study proposes a matching-on-followers method by combining choice and cascade models. More specifically, the study examines the impacts of interaction frequency, multiple exposures, and interest similarity between parent users and potential retweeters on the likelihood of retweeting. The study also incorporates the depth of diffusion cascades and network structures into the model. By using a random sample of original tweets, their retweets, and potential retweeters (N = 87,139), the study found that cascade depth is negatively associated with social contagion effects (interaction and multiple exposures) and positively associated with the effect of interest similarity on message sharing. These results indicate that influence-based and homophily-driven diffusion operate differently in cascades with different diffusion structures.

对转发行为进行建模对于理解和预测信息在社交媒体平台上的传播方式非常重要。本研究通过研究随着扩散级联深度的减少的社会传染和同构效应的增加，为文献做出了贡献。为了检验假设，该研究提出了一种通过选择和级联模型相结合的跟随者匹配方法。更具体地说，该研究考察了互动频率，多次曝光以及父母用户和潜在转发者之间的兴趣相似性对转发的可能性的影响。该研究还将扩散级联和网络结构的深度纳入模型。通过使用原始推文，其转发和潜在的转发者的随机样本（N （= 87,139），研究发现级联深度与社交传染效应（互动和多重接触）呈负相关，与兴趣相似性对消息共享的影响呈正相关。这些结果表明，基于影响和同构驱动的扩散在具有不同扩散结构的级联中的运行方式不同。