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Characterizing interactions in online social networks during exceptional events

机译：表征异常事件期间在线社交网络中的互动

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摘要

Nowadays, millions of people interact on a daily basis on online social media like Facebook and Twitter, where they share and discuss information about a wide variety of topics. In this paper, we focus on a specific online social network, Twitter, and we analyze multiple datasets each one consisting of individuals' online activity before, during and after an exceptional event in terms of volume of the communications registered. We consider important events that occurred in different arenas that range from policy to culture or science. For each dataset, the users' online activities are modeled by a multilayer network in which each layer conveys a different kind of interaction, specifically: retweeting, mentioning and replying. This representation allows us to unveil that these distinct types of interaction produce networks with different statistical properties, in particular concerning the degree distribution and the clustering structure. These results suggests that models of online activity cannot discard the information carried by this multilayer representation of the system, and should account for the different processes generated by the different kinds of interactions. Secondly, our analysis unveils the presence of statistical regularities among the different events, suggesting that the non-trivial topological patterns that we observe may represent universal features of the social dynamics on online social networks during exceptional events.
机译：如今，数以百万计的人们每天都在在线社​​交媒体（如Facebook和Twitter）上进行互动，他们在此共享和讨论有关各种主题的信息。在本文中，我们关注于一个特定的在线社交网络Twitter，并且我们分析了多个数据集，每个数据集都包含一个人在异常事件发生之前，之中和之后的在线活动，包括注册的通信量。我们考虑发生在从政策到文化或科学的不同领域中的重要事件。对于每个数据集，用户的在线活动都由一个多层网络建模，其中每个层都传达一种不同类型的交互方式，特别是：转发，提及和回复。这种表示形式使我们能够揭示出，这些不同类型的交互会产生具有不同统计属性的网络，尤其是有关度数分布和聚类结构的网络。这些结果表明，在线活动模型不能丢弃该系统的多层表示所携带的信息，而应说明由不同种类的交互产生的不同过程。其次，我们的分析揭示了不同事件之间统计规律的存在，这表明我们观察到的非平凡的拓扑模式可能代表异常事件期间在线社交网络上社交动态的普遍特征。

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