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首页> 外文期刊>Computational Social Systems, IEEE Transactions on >A Social Sensing Model for Event Detection and User Influence Discovering in Social Media Data Streams
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A Social Sensing Model for Event Detection and User Influence Discovering in Social Media Data Streams

机译：社交媒体数据流中发现和用户影响的社会传感模型

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摘要

Online social networks (OSNs) have emerged as a major platform for sharing information through social relationships and are one of the major sources of big data. Social networks can even accommodate sharing of live streaming data among the connected users. However, social information on social networks is often locally exploited rather than capturing the changes in the entire network over time. Obtaining user's influence statistics is limited only in their local vicinity, which may not facilitate capturing the changes in the user and post influences across the entire network, thereby resulting in lower accuracy while measuring user's topical influence. Moreover, low-influence users always exist in the network publishing low-quality posts. With the objectives of accurately capturing highly influential users and posts, this article proposes a novel dynamic social sensing model, named dynamic PageRank (DPRank) model, to evaluate the dynamic topical influence of the users of social information on social networks during the social information evolution. We deploy our proposed model to real-world Twitter data sets, which demonstrates the effectiveness of our proposed model against notable existing methods while identifying the true influence of users and posts in a dynamically evolving social network.
机译：在线社交网络（OSNS）已成为通过社会关系共享信息的主要平台，并且是大数据的主要来源之一。社交网络甚至可以在连接的用户之间容纳分享实时流数据。然而，关于社交网络的社交信息通常是在本地开发的，而不是随着时间的推移捕获整个网络的变化。获取用户的影响统计仅限于其本地附近，这可能不促进在整个网络上捕获用户的变化和后期影响，从而在测量用户的局部影响时导致较低的准确性。此外，低影响用户始终存在于网络上的低质量柱中。凭借准确地捕获高度影响力的用户和职位的目标，本文提出了一种新颖的动态社会传感模型，命名为动态Pagerank（DPRANK）模型，以评估社交信息进化期间社交网络中社会信息用户的动态局部影响。我们将我们提出的模型部署到现实世界的Twitter数据集，这展示了我们提出模型对显着现有方法的有效性，同时识别了用户和帖子在动态不断发展的社交网络中的真正影响。

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