Hashtags are widely used for communication in online media. As a condensed version of information, they characterize topics and discussions. For their analysis, we apply methods from network science and propose novel tools for tracing their dynamics in time-dependent data. The observations are characterized by bursty behaviors in the increases and decreases of hashtag usage. These features can be reproduced with a novel model of dynamic rankings. We build temporal and weighted co-occurrence networks from hashtags. On static snapshots, we infer the community structure using customized methods. On temporal networks, we solve the bipartite matching problem of detected communities at subsequent timesteps by taking into account higher-order memory. This results in a matching protocol that is robust toward temporal fluctuations and instabilities of the static community detection. The proposed methodology is broadly applicable and its outcomes reveal the temporal behavior of online topics. We consider the size of the communities in time as a proxy for online popularity dynamics. We find that the distributions of gains and losses, as well as the interevent times are fat-tailed indicating occasional, but large and sudden changes in the usage of hashtags. Inspired by typical website designs, we propose a stochastic model that incorporates a ranking with respect to a time-dependent prestige score. This causes occasional cascades of rank shift events and reproduces the observations with good agreement. This offers an explanation for the observed dynamics, based on characteristic elements of online media.

中文翻译：

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随着时间的推移跟踪在线主题：了解动态标签社区。

主题标签广泛用于在线媒体中的交流。作为信息的浓缩版本，它们表征主题和讨论。对于他们的分析，我们应用了网络科学的方法，并提出了新的工具来追踪他们在时间相关数据中的动态。这些观察的特点是标签使用的增加和减少中的突发行为。这些特征可以通过一种新颖的动态排名模型来重现。我们从主题标签构建时间和加权共现网络。在静态快照上，我们使用自定义方法推断社区结构。在时间网络上，我们通过考虑高阶记忆来解决后续时间步检测到的社区的二分匹配问题。这导致匹配协议对静态社区检测的时间波动和不稳定性具有鲁棒性。所提出的方法具有广泛的适用性，其结果揭示了在线主题的时间行为。我们将社区的规模及时视为在线流行动态的代表。我们发现收益和损失的分布以及事件时间是肥尾的，表明主题标签的使用偶尔会发生巨大而突然的变化。受典型网站设计的启发，我们提出了一个随机模型，该模型结合了与时间相关的声望分数的排名。这会导致偶尔级联的秩移位事件，并以良好的一致性重现观察结果。这为观察到的动态提供了解释，