Uncovering the Spatio-Temporal Dynamics of Memes in the Presence of Incomplete Information
Uncovering the Spatio-Temporal Dynamics of Memes in the Presence of Incomplete Information
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DOI：
10.1145/2983323.2983782
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Hancheng Ge;James Caverlee;N. Zhang;A. Squicciarini
中科院分区：
文献类型：
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作者：
Hancheng Ge;James Caverlee;N. Zhang;A. Squicciarini
建模、理解和预测在线模因的时空动态是一项重要的任务，对基于位置的服务、社交媒体搜索、定向广告和内容交付网络都有影响。然而，揭示这些动态的原始数据通常是不完整且容易出错的；例如，API限制和数据采样策略可能导致对这些动态的不完整(且往往是有偏见的)观点。因此，在本文中，我们通过一个新的时空动态恢复框架来研究揭示完整(潜在)分布的新方法，该框架模拟了位置、模因和时间之间的潜在关系。通过将这些隐藏的关系整合到一个基于张量的恢复框架--称为AirCP--我们发现，只需访问完整数据的一小部分，就可以构建高质量的模因传播模型。在合成和真实的Twitter标签数据上的实验结果表明，该框架具有良好的性能：在恢复标签的时空动态方面，与现有的五种替代方案相比，平均提高了27%以上。
Modeling, understanding, and predicting the spatio-temporal dynamics of online memes are important tasks, with ramifications on location-based services, social media search, targeted advertising and content delivery networks. However, the raw data revealing these dynamics are often incomplete and error-prone; for example, API limitations and data sampling policies can lead to an incomplete (and often biased) perspective on these dynamics. Hence, in this paper, we investigate new methods for uncovering the full (underlying) distribution through a novel spatio-temporal dynamics recovery framework which models the latent relationships among locations, memes, and times. By integrating these hidden relationships into a tensor-based recovery framework -- called AirCP -- we find that high-quality models of meme spread can be built with access to only a fraction of the full data. Experimental results on both synthetic and real-world Twitter hashtag data demonstrate the promising performance of the proposed framework: an average improvement of over 27% in recovering the spatio-temporal dynamics of hashtags versus five state-of-the-art alternatives.