Mitigating the Impact of Data Sampling on Social Media Analysis and Mining
Mitigating the Impact of Data Sampling on Social Media Analysis and Mining
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DOI：
10.1109/tcss.2020.2970602
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发表时间：
2020-02
影响因子：
5
通讯作者：
Kuai Xu;Feng Wang;Haiyan Wang;Yufang Wang;Ying Zhang
中科院分区：
文献类型：
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作者：
Kuai Xu;Feng Wang;Haiyan Wang;Yufang Wang;Ying Zhang
过去十年见证了在线社交媒体在用户和内容方面的爆炸式增长。由于前所未有的规模和底层社交网络的级联力量，社交媒体创造了一种新的模式，可以让任何用户在任何时间、任何地点分享信息、广播突发新闻和报道实时事件。包括Twitter在内的许多流行的社交媒体网站通过标准api向广泛的研究人员和开发人员社区提供流数据服务。考虑到在线社交媒体的庞大数据量、快速速度和特征多样性，这些网站通常只提供一组采样的流数据，而不是完整的数据集，以减少计算、存储和网络带宽的资源成本。鉴于采样对Twitter数据流的重大影响，本文探讨了光谱聚类、位置敏感哈希（LSH）、潜在狄利克雷分配（LDA）主题建模和微分方程建模的结合，以减轻采样对社交媒体数据分析的影响，特别是在检测现实世界事件和预测信息扩散方面。我们的大量实验表明，我们提出的方法能够有效地检测实时出现的事件，并从1%的Twitter数据流中准确地预测这些事件的级联模式。据我们所知，这篇文章是第一次尝试引入一种系统的方法来研究和减轻数据抽样对社交媒体分析和挖掘的影响。
The last decade has witnessed the explosive growth of online social media in users and contents. Due to the unprecedented scale and the cascading power of the underlying social networks, social media has created a new paradigm for sharing information, broadcasting breaking news, and reporting real-time events by any user from anywhere at any time. Many popular social media sites including Twitter provide streaming data services by standard APIs to the broad researcher and developer communities. Given the sheer data volume, rapid velocity, and feature variety of online social media, these sites often supply only a sampled set of streaming data, rather than the full data set to reduce the resource cost of computations, storage, and network bandwidth. In light of the substantial impact of sampling in Twitter data stream, this article explores a combination of spectral clustering, locality-sensitive hashing (LSH), latent Dirichlet allocation (LDA) topic modeling, and differential equation modeling to mitigate the impact of sampling on social media data analysis, in particular on detecting real-world events and predicting information diffusion. Our extensive experiments demonstrate that our proposed method is able to detect effectively the real-time emerging events and predict accurately the cascading pattern of these events from the 1% sampled Twitter data stream. To the best of our knowledge, this article is the first effort to introduce a systematic methodology to study and mitigate the impact of data sampling on social media analysis and mining.