[论文解读] Meme creation and sharing processes: individuals shaping the masses

Ian D. Miller, Gerald C. Cupchik|arXiv (Cornell University)|Jun 30, 2014

Opinion Dynamics and Social Influence参考文献 7被引用 4

一句话总结

本研究通过分别对创作过程与分享过程进行建模，探究了个体表情包创作者与分享者如何影响网络表情包的传播。通过实验室实验、网络日志追踪及统计建模，研究发现创作者特征可解释11.5%的再分享行为方差，内容/反应因素则解释了37.5%的分享决策，表明个体行为共同驱动了病毒式传播。

ABSTRACT

The propagation of online memes is initially influenced by meme creators and secondarily by meme consumers, whose individual sharing decisions accumulate to determine total meme propagation. We characterize this as a sender/receiver sequence in which the first sender is also the creator. This sequence consists of two distinct processes, the creation process and the sharing process. We investigated these processes separately to determine their individual influence on sharing outcomes. Our study observed participants creating memes in the lab. We then tracked the sharing of those memes, derived a model of sharing behavior, and implemented our sharing model in a contagion simulation. Although we assume meme consumers typically have little or no information about a meme's creator when making a decision about whether to share a meme (and vice versa), we nevertheless ask whether consumer re-sharing behavior can be predicted based on features of the creator. Using human participants, web log monitoring, and statistical model fitting, the resulting Creator Model of Re-sharing Behavior predicts 11.5% of the variance in the behavior of consumers. Even when we know nothing about re-sharers of a meme, we can predict something about their behavior by observing the creation process. To investigate the individual re-sharing decisions that, together, constitute a meme's total consumer response, we built a statistical model from human observation. Receivers make their decision to share as a function of the meme's content and their reaction to it, which we model as a consumer's decision to share. The resulting Consumer Model of Sharing Decisions describes 37.5% of the variance in this decision making process.

研究动机与目标

• 理解个体在表情包创作与分享中的决策如何共同影响整体传播效果。
• 将创作过程与分享过程分离并建模为相互关联但独立的阶段。
• 确定是否可根据表情包创作者的特征预测再分享行为，即使不了解分享者本身。
• 基于实证数据，量化内容与用户反应对分享决策的影响。
• 开发并验证基于观察到的分享行为的传染性模拟模型。

提出的方法

• 在受控实验室环境中开展实验，参与者创作原创表情包以研究创作过程。
• 通过网络日志监控追踪所创作表情包的真实世界分享行为，收集实证数据。
• 对观察到的分享行为拟合统计模型，基于内容与用户反应推导出分享决策的消费者模型。
• 通过分析创作者特定特征如何影响下游分享行为，构建再分享行为的创作者模型。
• 将两个模型整合至传染性模拟中，以测试并验证整体传播动态。
• 使用决定系数（R²）作为评估各模型预测能力的指标。

实验结果

研究问题

• RQ1 尽管缺乏对分享者的了解，是否仍可根据表情包创作者的特征预测再分享行为？
• RQ2 内容与用户反应在多大程度上影响个体分享表情包的决策？
• RQ3 分享结果中的方差有多少可归因于创作过程，又有多少可归因于分享过程？
• RQ4 基于实证推导的模型能否准确复现现实世界中的表情包传播模式？
• RQ5 分享过程中的个体决策如何共同决定表情包的总体传播规模？

主要发现

• 再分享行为的创作者模型可解释消费者再分享行为11.5%的方差，表明创作者特征对分享结果具有显著影响。
• 分享决策的消费者模型可解释个体分享决策37.5%的方差，凸显内容与个人反应的关键作用。
• 即使不了解再分享者，仅凭创作过程本身即可对下游分享行为提供预测能力。
• 本研究证明，创作与分享过程虽独立但相互依赖，对整体传播具有可测量的统计影响。
• 将两个模型整合进传染性模拟后，成功复现了现实世界的分享动态，验证了模型框架的有效性。
• 结果表明，创作与分享过程中的个体决策在理解大规模表情包扩散中具有关键作用。