通过有限观测检测流行的社会事件：深度生存分析
Detecting Popular Social Events through Limited Observation with Deep Survival Analysis
AI总结：
研究通过深度生存分析方法，利用有限早期数据预测社交网络中的流行趋势，提升推荐系统和数字营销决策能力。
摘要：
用户在各种社交网络上的活动增加使其成为个体之间交换和传播信息的主要平台。为了在网络中传播信息，用户最初在社交网络上分享信息，然后直接接触他的用户可能分享该信息。信息通过重复此过程在整个网络中扩展。一组变得流行并被不同个体反复分享的信息被称为流行趋势。识别和分析这些趋势为了解网络内信息传播动态提供了有价值的见解。然而，更重要的是，主动方法出现。换句话说，通过观察信息在扩展早期阶段的传播模式，可以确定该传播是否会在未来变得流行。本研究旨在通过观察有限的早期数据并使用基于深度生存分析的方法来预测和检测社交网络中的流行趋势。该模型在提升推荐系统、预测数字内容的传播范围以及协助数字营销中的最佳决策方面发挥重要作用。最终，所提出的方法在来自Twitter、Weibo和Digg的多个真实匿名数据集上进行了测试。
英文摘要：
Users increasing activity across various social networks made it the most widely used platform for exchanging and propagating information among individuals. To spread information within a network, a user initially shared information on a social network, and then other users in direct contact with him might have shared that information. Information expanded throughout the network by repeatedly following this process. A set of information that became popular and was repeatedly shared by different individuals was called popular trends. Identifying and analyzing these trends led to valuable insights into the dynamics of information dissemination within a network. However, more importantly, proactive approaches emerged. In other words, by observing the dissemination pattern of a piece of information in the early stages of expansion, it became possible to determine whether this cascade would become highly popular in the future. This research aimed to predict and detect popular trends in social networks by observing limited early-stage data and using a deep survival analysis-based method. This model could play a significant role in improving recommendation systems, predicting the reach of digital content, and assisting in optimal decision-making in digital marketing. Ultimately, the proposed method was tested on various real-world anonymized datasets from Twitter, Weibo, and Digg.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
