异质社会网络信息可信度评估与建模研究

项目名称： 异质社会网络信息可信度评估与建模研究

项目编号： No.61300148

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 王英

作者单位： 吉林大学

项目金额： 25万元

中文摘要： 当前，异质社会网络信息可信度评估主要依靠人工操作，但这种方式在大众信息互动交流时，缺乏有效的处理机制。因此，本课题立足异质社会网络自身特点从个体认知心理特征和可信与不可信群体的集体智慧角度研究信息可信度评估模型：首先，拟在基于认知和语义规则推理信任关系的基础上，分析用户个人品性，预测用户情感状态和理性状态，从而获取异质社会网络中个体认知心理特征。其次，拟利用人工免疫系统识别可信与不可信群体，进而实现对用户信誉度、社会影响程度和群体认同程度的量化，为信息可信度评估提供属性证据。最后，拟根据个人品性、情感状态、理性状态、信誉度、社会影响程度、可信与不可信群体对待评估信息的认同程度6个领域下的属性证据，构建基于证据理论的多源属性证据信任融合模型。在充分考虑用户个人隐私的前提下，拟从Epinions和Twitter两个数据源获取实验数据，按照社会学和认知心理学已有的理论基础抽取特征属性，并验证其在模型中有效性，最终实现对异质社会网络信息可信度的评估。本课题考虑了信息可信度的动态性、主观性和多源性建模，为异质社会网络信息可信度评估研究提供了新思路和理论依据。

中文关键词： 异质社会网络；信息可信度；人工免疫系统；证据理论；矩阵分解

英文摘要： Currently, the evaluation of information credibility in heterogeneous social networks mainly relies on manual operation, however，it shows a lack of efficient handling mechanism in information communication. Therefore, the study is based on the characteristics of heterogeneous social networks, and is carried out from the perspective of individual cognitive and psychological characteristics, and collective wisdom from trust and distrust groups to analyze the evaluation model of information credibility: Firstly, for the purpose of acquiring individual cognitive and psychological characteristics in heterogeneous social networks, this study intends to analyze user's personality, predict user's emotional state and rational state based on reasoning trust relationships from the rules of cognition and semantics. Secondly, in order to provide property evidences for evaluating information credibility, this study intends to identify the trust and distrust groups by importing artificial immune system, and then quantify users' reputation, social influence and group support degree. Finally, the study will construct the Credibility Fusion Model based on Multi-source Property Evidences by merging these property evidences from personality, emotional state, rational state, reputation, social influence and the information emotiona

英文关键词： Heterogeneous Social Networks；Information Credibility；Artificial Immune System；Evidence Theory；Matrix Factorization