Abstract 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.

中文翻译：

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社交媒体平台上的传播：相似内容之间交互的点过程模型

摘要 社交媒体平台传播了大量用户生成的内容，其中一些传达了相似和重叠的信息。我们研究给定内容的传播（称为级联）如何受到携带类似内容的其他级联（称为并行级联）的扩散的影响。我们的理论是，级联的扩散可以被包含相似内容的平行级联的扩散抑制或放大。为了研究这种现象，我们制定了自激点过程模型的广义版本，并展示了一种评估类似社交媒体内容并行传播的新方法。我们使用 Twitter 数据估计模型。我们观察到，平均而言，级联的扩散受到具有相似内容的并行级联的并发扩散的抑制。我们进一步确定了内容生产者之间的不对称性，因为具有较大网络的那些人贡献的内容传播更有可能被类似内容的传播放大。我们的研究强调了考虑内容相似性的重要性，因为不这样做可能会高估对级联扩散的评估。我们的结果还表明，较小的个人社交媒体内容贡献者应避免发布重复内容，并将其努力用于开发新颖的内容，而这对于较大的内容贡献者来说不是问题。我们的研究强调了考虑内容相似性的重要性，因为不这样做可能会高估对级联扩散的评估。我们的结果还表明，较小的个人社交媒体内容贡献者应避免发布重复内容，并将其努力用于开发新颖的内容，而这对于较大的内容贡献者来说不是问题。我们的研究强调了考虑内容相似性的重要性，因为不这样做可能会高估对级联扩散的评估。我们的结果还表明，较小的个人社交媒体内容贡献者应避免发布重复内容，并将其努力用于开发新颖的内容，而这对于较大的内容贡献者来说不是问题。