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基于马尔可夫逻辑网络识别网络舆论领袖。

Identifying network public opinion leaders based on Markov Logic Networks.

作者信息

Zhang Weizhe, Li Xiaoqiang, He Hui, Wang Xing

机构信息

School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.

出版信息

ScientificWorldJournal. 2014;2014:268592. doi: 10.1155/2014/268592. Epub 2014 Apr 1.

DOI:10.1155/2014/268592
PMID:24977188
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3995098/
Abstract

Public opinion emergencies have important effect on social activities. Recognition of special communities like opinion leaders can contribute to a comprehensive understanding of the development trend of public opinion. In this paper, a network opinion leader recognition method based on relational data was put forward, and an opinion leader recognition system integrating public opinion data acquisition module, data characteristic selection, and fusion module as well as opinion leader discovery module based on Markov Logic Networks was designed. The designed opinion leader recognition system not only can overcome the incomplete data acquisition and isolated task of traditional methods, but also can recognize opinion leaders comprehensively with considerations to multiple problems by using the relational model. Experimental results demonstrated that, compared with the traditional methods, the proposed method can provide a more accurate opinion leader recognition and has good noise immunity.

摘要

舆情突发事件对社会活动有着重要影响。识别意见领袖等特殊群体有助于全面了解舆情发展趋势。本文提出了一种基于关系数据的网络意见领袖识别方法，并设计了一个舆情领袖识别系统，该系统集成了舆情数据采集模块、数据特征选择与融合模块以及基于马尔可夫逻辑网络的意见领袖发现模块。所设计的意见领袖识别系统不仅能够克服传统方法数据采集不完整和任务孤立的问题，还能通过使用关系模型综合考虑多个问题来识别意见领袖。实验结果表明，与传统方法相比，该方法能够提供更准确的意见领袖识别结果，并且具有良好的抗噪声能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/916d4f858dc0/TSWJ2014-268592.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/60e8801d8e0b/TSWJ2014-268592.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/35283936fb47/TSWJ2014-268592.002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/3583731a780d/TSWJ2014-268592.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/c2c0956f6468/TSWJ2014-268592.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/61511c4c4893/TSWJ2014-268592.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/59889c362ee6/TSWJ2014-268592.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/916d4f858dc0/TSWJ2014-268592.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/60e8801d8e0b/TSWJ2014-268592.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/35283936fb47/TSWJ2014-268592.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/ca288d69308a/TSWJ2014-268592.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/5814fb625c9f/TSWJ2014-268592.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/3583731a780d/TSWJ2014-268592.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/c2c0956f6468/TSWJ2014-268592.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/61511c4c4893/TSWJ2014-268592.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/59889c362ee6/TSWJ2014-268592.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bfc/3995098/916d4f858dc0/TSWJ2014-268592.009.jpg

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