Interaction Models for Detecting Nodal Activities in Temporal Social Media Networks
Interaction Models for Detecting Nodal Activities in Temporal Social Media Networks
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DOI：
10.1145/3365537
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Wingyan Chung;Bingbing Rao;Liqiang Wang
中科院分区：
文献类型：
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作者：
Wingyan Chung;Bingbing Rao;Liqiang Wang
在动态社会网络中检测节点活动在许多应用中具有战略重要性，例如在线营销活动和国土安全监视。社交媒体中的点对点交换如何促进节点活动检测还没有得到很好的探索。现有模型假设网络节点在时间上是静态的，没有充分考虑社会理论的特征。本研究建立并验证了随机交互模型（RIM）和优先交互模型（PIM）这两个基于理论的模型，以表征人类智能体社交媒体网络中的时间节点活动。这些模型捕捉了由于社区规模、人为偏见、连接成本下降和可达性上升而导致的随机性和优先交互的网络特征。这些模型与三个基准模型（缩写为EAM， TAM和DBMM）进行了比较，使用了一个由790,462名用户组成的社交媒体社区，这些用户在2013-2015年间发布了超过3,286,473条推文，并形成了超过3,055,797个链接。实验结果表明，RIM和PIM在不同日期和时间窗的精度上都明显优于EAM和TAM。PIM和RIM的误差都明显小于DBMM。研究发现，社会网络的结构特性为预测模型性能提供了一种简单而准确的方法。这些结果表明，该模型在考虑现实世界社交媒体网络中的用户交互和时间活动检测方面具有很强的能力。研究应提供时间网络活动检测的新方法，制定相关的新措施，并报告来自大型社交媒体数据集的新发现。
Detecting nodal activities in dynamic social networks has strategic importance in many applications, such as online marketing campaigns and homeland security surveillance. How peer-to-peer exchanges in social media can facilitate nodal activity detection is not well explored. Existing models assume network nodes to be static in time and do not adequately consider features from social theories. This research developed and validated two theory-based models, Random Interaction Model (RIM) and Preferential Interaction Model (PIM), to characterize temporal nodal activities in social media networks of human agents. The models capture the network characteristics of randomness and preferential interaction due to community size, human bias, declining connection cost, and rising reachability. The models were compared against three benchmark models (abbreviated as EAM, TAM, and DBMM) using a social media community consisting of 790,462 users who posted over 3,286,473 tweets and formed more than 3,055,797 links during 2013–2015. The experimental results show that both RIM and PIM outperformed EAM and TAM significantly in accuracy across different dates and time windows. Both PIM and RIM scored significantly smaller errors than DBMM did. Structural properties of social networks were found to provide a simple and yet accurate approach to predicting model performances. These results indicate the models’ strong capability of accounting for user interactions in real-world social media networks and temporal activity detection. The research should provide new approaches for temporal network activity detection, develop relevant new measures, and report new findings from large social media datasets.