论文标题
显然：与霍克斯流程进行建模和分析Reshare Cascades
Evently: Modeling and Analyzing Reshare Cascades with Hawkes Processes
论文作者
论文摘要
建模在线话语动态是了解信息传播的核心活动，包括离线和在线以及新兴的在线行为。目前，在线社交媒体分析的从业者（通常是社交，政治和传播科学家）与能够检查用户在线讨论的工具之间存在脱节。在这里，我们显然是使用自我激发的过程来建模在线重新出现级联反应，尤其是转发级联的工具。它提供了一组全面的功能，用于从Twitter公共API中处理原始数据，对已处理转推级联的时间动态进行建模，并通过广泛的扩散措施来表征在线用户。该工具专为具有广泛计算机专业知识的研究人员而设计，其中包括教程和详细文档。我们通过对COVID-19的局部数据集上的在线用户行为进行端到端分析来说明使用的用法。我们表明，通过仅根据用户的内容在线传播的方式来表征用户，我们可以删除有影响力的用户和在线机器人。
Modeling online discourse dynamics is a core activity in understanding the spread of information, both offline and online, and emergent online behavior. There is currently a disconnect between the practitioners of online social media analysis -- usually social, political and communication scientists -- and the accessibility to tools capable of examining online discussions of users. Here we present evently, a tool for modeling online reshare cascades, and particularly retweet cascades, using self-exciting processes. It provides a comprehensive set of functionalities for processing raw data from Twitter public APIs, modeling the temporal dynamics of processed retweet cascades and characterizing online users with a wide range of diffusion measures. This tool is designed for researchers with a wide range of computer expertise, and it includes tutorials and detailed documentation. We illustrate the usage of evently with an end-to-end analysis of online user behavior on a topical dataset relating to COVID-19. We show that, by characterizing users solely based on how their content spreads online, we can disentangle influential users and online bots.
