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The Importance of Interactions Between Content Characteristics and Creator Characteristics for Studying Virality in Social Media

机译:社会媒体中患者特征与创作者特征之间相互作用的重要性

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With the ubiquity of social media usage and influence, the phenomenon of virality-that is, large-scale diffusion and sharing of an online post-has received considerable scrutiny. Research on virality is of primarily two types. Content-based research focuses on how content characteristics influence virality, whereas creator-based research uses characteristics of the creator of the content to study virality. Through this research note, we aim to draw attention toward a relatively ignored set of constructs-the interactions between content characteristics and creator characteristics. We propose a typology of content features and content message on one hand and creator features and creator history on the other. We argue that adding nuanced content-creator interactions to the nomological network for virality will add conceptual richness and improve predictive validity of future studies. We demonstrate this by running models, with and without the interactions, on a data set of nearly 800,000 posts from Twitter. We find that many of these interactions are significant, improve goodness of fit by 20%, provide clues about contextual factors in virality, and boost predictive power by 12%. Our results and subsequent discussions of the findings hope to spur more research on content-creator interactions in understanding virality.
机译:随着社交媒体使用和影响的无处不在,观点的现象 - 即大规模的扩散和在线发布的分享 - 已获得相当大的审查。对景观的研究主要是两种类型。基于内容的研究侧重于内容特征如何影响病毒性,而基于创建的研究使用内容创作者的特征来研究病毒性。通过这项研究说明,我们的目标是引起对相对忽略的构造 - 内容特征与创造特征之间的相互作用。我们在一方面提出了内容功能和内容消息的类型,而另一方面的创建功能和创建者历史记录。我们争辩说,增加对遗传学网络的详细资料网络的细节内容创作者相互作用将增加概念性的丰富性,并提高未来研究的预测有效性。我们通过在从Twitter的近80,000个帖子的数据集上运行模型,使用和没有互动来演示这一点。我们发现许多这些相互作用是显着的,提高良好的良好20%,提供有关病毒中的上下文因素的线索,并提高预测功率12%。我们的结果和随后对调查结果的讨论希望促进更多研究内容创建者在理解病毒中的相互作用。

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