Identifying Influential Brokers on Social Media from Social Network Structure
Identifying Influential Brokers on Social Media from Social Network Structure
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DOI：
10.48550/arxiv.2208.00630
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Sho Tsugawa;Kohei Watabe
中科院分区：
文献类型：
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作者：
Sho Tsugawa;Kohei Watabe
识别特定社交网络中的影响者已成为各种应用的重要研究问题，包括加速病毒式营销中的信息传播，防止假新闻和谣言的传播。这些文献包含了大量关于识别有影响力的来源传播者的研究，这些传播者可以将自己的信息传播到许多其他节点。相比之下，识别哪些有影响力的经纪人可以将其他节点的消息传播到许多节点上还没有得到充分的探索。理论和实证研究表明，有影响力的来源传播者和经纪商的参与是促进大规模信息扩散级联的关键。因此，本文探索了从给定的社会网络中识别有影响力的经纪人的方法。通过使用三个社交媒体数据集，我们通过比较有影响力的源传播者和中心性度量得到的中心节点来考察有影响力的经纪人的特征。我们的结果表明：(I)大多数有影响力的源传播者不是有影响力的经纪人(反之亦然)；(Ii)在Twitter数据集中，中心节点和有影响力的经纪人之间的重叠度很小(不到15%)。我们还解决了从中心性度量和节点嵌入来识别有影响力的经纪人的问题，并检验了社会网络特征在经纪人识别任务中的有效性。研究结果表明：(3)虽然单一的中心性度量不能很好地刻画有影响力的经纪人，但使用节点嵌入特征的预测模型的F1得分为0.35-0.68，这表明社会网络特征对于识别有影响力的经纪人是有效的。
Identifying influencers in a given social network has become an important research problem for various applications, including accelerating the spread of information in viral marketing and preventing the spread of fake news and rumors. The literature contains a rich body of studies on identifying influential source spreaders who can spread their own messages to many other nodes. In contrast, the identification of influential brokers who can spread other nodes' messages to many nodes has not been fully explored. Theoretical and empirical studies suggest that involvement of both influential source spreaders and brokers is a key to facilitating large-scale information diffusion cascades. Therefore, this paper explores ways to identify influential brokers from a given social network. By using three social media datasets, we investigate the characteristics of influential brokers by comparing them with influential source spreaders and central nodes obtained from centrality measures. Our results show that (i) most of the influential source spreaders are not influential brokers (and vice versa) and (ii) the overlap between central nodes and influential brokers is small (less than 15%) in Twitter datasets. We also tackle the problem of identifying influential brokers from centrality measures and node embeddings, and we examine the effectiveness of social network features in the broker identification task. Our results show that (iii) although a single centrality measure cannot characterize influential brokers well, prediction models using node embedding features achieve F1 scores of 0.35--0.68, suggesting the effectiveness of social network features for identifying influential brokers.