Identifying Event-specific Opinion Leaders by Local Weighted LeaderRank
Identifying Event-specific Opinion Leaders by Local Weighted LeaderRank
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DOI：
10.32604/iasc.2020.012480
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发表时间：
2020
影响因子：
2
通讯作者：
Wanxian Yang;Sadaqat Rehman;Wenhui Que
中科院分区：
文献类型：
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作者：
Wanxian Yang;Sadaqat Rehman;Wenhui Que
：识别特定事件的意见领袖对于了解事件发展和影响公众舆论至关重要。新闻文章内容丰富、表达正式，包含有关特定事件的有价值的信息。在本文中，我们提出了 LeaderRank 的改进变体，称为局部加权 LeaderRank，用于测量使用与特定事件相关的新闻文章构建的加权无向人物共现网络中人物节点的特定于事件的影响。我们提出的方法通过考虑人之间的共现强度以及每个本地人节点的附加本地链接权重信息来衡量人节点的影响。为了评估我们方法的性能，我们使用加权易感感染（WSI）模型来模拟真人共现网络中的影响传播过程。测量模拟结果生成的排名列表与影响力度量生成的排名列表之间的排名相关性后获得的实验结果表明，我们的方法可以有效地识别特定事件的意见领袖，并且比其他最先进的影响力度量（例如加权 K-shell 分解和加权局部中心性）表现更好。根据模拟结果和使用不同影响力测量方法创建的排名列表创建。我们表明，我们的 LWLR 测量比 WDC、WKS 和 WCC 测量更精确地对节点的影响进行排名，同时它达到了与 WLC 相当的精度。此外，通过比较所提出的方法生成的排名列表中前L个节点的平均影响力和其他方法生成的平均影响力，我们证明我们的方法优于其他测试的影响力度量。我们还比较了我们的方法生成的排名列表中前10个节点与其他方法生成的排名列表中前10个节点的传播过程，结果表明我们的方法生成的排名列表中前10个节点具有更强的传播能力，可以更快、更广泛地传播其影响力，进一步验证了所提方法的有效性。最后，我们研究了根据模拟结果生成的排名列表和根据以下情况下的各种影响措施生成的排名列表之间计算得出的 Kendall tau 系数 ( ) 的值：
: Identifying event-specific opinion leaders is essential for understanding event developments and influencing public opinion. News articles are informative and formal in expression, and include valuable information on specific events. In this paper, we propose an improved variant of LeaderRank, called local weighted LeaderRank, to measure the event-specific influence of person nodes in a weighted and undirected person cooccurrence network constructed using news articles related to a specific event. Our proposed method measures the influence of person nodes by considering both the cooccurrence strength between persons, and additional local link weight information for each local person node. To evaluate the performance of our method, we use the weighted susceptible infected (WSI) model to simulate the influence-spreading process in real-person cooccurrence networks. The experiment results obtained after measuring the rank correlations between the rank list generated by the simulation results and those generated by the influence measures show that our method identifies event-specific opinion leaders effectively and performs better than other state-of-the-art influence measures, such as weighted K-shell decomposition and the weighted local centrality. created from the simulation results and the ranked lists created using different influence measurement methods. We show that our LWLR measure ranks the influences of nodes more precisely than do the WDC, WKS, and WCC measures do, while it achieves an accuracy comparable to that of the WLC. Furthermore, by comparing the average influence of the top-L nodes in the ranked lists generated by the proposed method and those generated by the other methods, we demonstrate that our method outperforms the other tested influence measures. We also compare the spreading processes of the top 10 nodes in the ranked lists generated by our method and in those generated by another method and show that the top 10 nodes in the ranked list generated by our method have a stronger spreading ability and can propagate their influence more quickly and broadly, further verifying the effectiveness of the proposed approach. Finally, we investigate the values of Kendall's tau coefficient ( ) as calculated between the ranked lists produced from the simulation results and the ranked lists produced based on the various influence measures when