Multi-agent based research of centralized false information discerning model of micro-blog network

Diao Hailun, Wang Shuyi, Zhang Guorui
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Abstract

As a newly popularized online social network, micro-blog has been facilitating numerous social interactions and communications in our daily life. Websites serving as micro-blog share certain characteristics of scale-free network with a fractal dimension macroscopically, and a faster spread of information has emerged by the hand of the least entropy generation principle through this complex system. False information, however, has impacted this new form of media to a noticeable extent. This paper is thus dedicated to finding the best way to discern false information amid the classical methods, a directed scale-free network is realized, whence a Micro-blog Central False information Discerning Model (MCFDM) including six different combinations of three user levels is proposed. After analyzing the outputs from Multi-Agent simulations on NetLogo, a constructive assessment of various methods will be conclusively rendered.
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基于多智能体的微博网络集中式虚假信息识别模型研究
作为一种新兴的在线社交网络,微博已经在我们的日常生活中促进了大量的社会互动和交流。作为微博的网站在宏观上具有分形维数的无标度网络的某些特征,通过这一复杂的系统,利用最小熵产生原理使信息得以更快地传播。然而,虚假信息在很大程度上影响了这种新媒体形式。因此,本文致力于在经典方法中寻找识别虚假信息的最佳方法,实现了一个有向无标度网络,并在此基础上提出了包含三个用户级别的六种不同组合的微博中心虚假信息识别模型(MCFDM)。在对NetLogo上的多智能体仿真输出进行分析后,将对各种方法进行建设性的评估。
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