A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Consensus model of social network group decision-making based on trust relationship among experts and expert reliability

Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.

Antecedents of Social Network Trust in SNS Usage: The Moderating Role of Offline Familiarity

With the rapid development of social networks, there is a focus on marketing strategies and business models that are based on social media. In the academic world, scholars believe that online trust is a key factor contributing to online purchasing behavior. This article explored several factors in social media trust and verified the moderating role of offline familiarity by using relevant research on online trust in conjunction with a structure equation model. The results show that independent variables such as reputation, SNS interaction, information quality, reciprocity, satisfaction and shared values have a positive influence on trust, whereas perceived similarity does not, and information quality is the most important factor. In addition, offline familiarity significantly moderates the relations between information quality, reciprocity, reputation, shared values and social media trust. This information is important to assist companies in developing an effective social network marketing strategy.

A Trust Evaluation Model for Social Commerce Based on BP Neural Network

Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial factor in the success of social commerce. Business factors, environment factors and social factors including twelve secondary indexes build up a social commerce trust evaluation model. Questionnaires are handed out to collect twelve secondary indexes scores as input of BP neural network and composite score of trust as output. Model simulation shows that both training samples and test samples have low level of average error and standard deviation, which certify that the model has good stability and it is a good method for evaluating social commerce trust.

New Ways of Self-defence Society： The Role of Trust in Social Networks

The development of the Internet coincides with 90s financial crises. The net seemed to realize what Karl Polanyi defined social self-defence from the capitalism against the transformation of labour, land and money in fictitious commodities. In this paper we try to consider how Internet, and in particular social network, has modified many deep aspects of our life. With two different approaches, sociological and philosophical, we try to understand how social networks support new shapes of happiness and trust in an economic crisis context.

A Personalized Recommendation Algorithm with User Trust in Social Network

In the era of big data, personalized recommendation has become an important research issue in social networks as it can find and match user’s preference. In this paper, the user trust is integrated into the recommendation algorithm, by dividing the user trust into 2 parts: user score trust and user preference trust. In view of the common items in user item score matrix, the algorithm combines the number of items with the score similarity between users, and establishes an asymmetric trust relationship matrix so as to calculate the user’s score trust. For the non common score items, we use the attribute information of items and the scoring weight to calculate the user’s preference trust. Based on the user trust in social network, a new collaborative filtering recommendation algorithm is proposed. Besides, a new matrix factorization recommendation algorithm is proposed by combining the user trust with matrix factorization. We did the experiments comparing with the related algorithms on the real data sets of social network. The results show that the proposed algorithms can effectively improve the accuracy of recommendation.

Influence of Trust on Social Commercial Behavior-Taking WeChat as an Example

The development of science and technology as well as the internet have brought changes to our daily lives.In addition,with the widespread use of social media,more and more people are using social platforms to connect with colleagues and serve business activities.This study takes WeChat,a specific social media platform in China,as an example to study how personal social relations influence personal consumption behaviour in the digital media era through WeChat users'daily use experience.This study adopts a mixed method.First,it tests users' perception based on cognitive and emotional factors through 122 questionnaire surveys.Then,the users'experiences from their participation in social enterprises are gathered through 10 semi-structured interviews,and subsequently,the relationship between personal relations and social enterprises are analyzed.Finally,after data collation and analysis,it can be concluded that trust is the core relationship quality and also the basis for promoting the development of social business activities.At the same time,since social business activities rely on social relations,the development of swift guanxi is conducive to the realization of repurchase behaviours in social business relations.

Social Media Shopping as a Driver for Brand Trust and Brand Commitment

Globalization and developments in digital technology paved the way for online communication,mobile penetration,and social media.Digital platforms and particularly social media have become popular sources of news and online interaction.Literature review so far reveals more than one billion social media users exist globally and use social media for shopping purposes.Hence,social media has become one of the most popular tools companies using for brand relationship building activities.The effect of social media on building customer commitment needs to be explored.This article aims to identify social media use among Turkish 18-40 years old in building commitment towards their favorite brands.

Deep Learning Enabled Social Media Recommendation Based on User Comments

Nowadays,review systems have been developed with social media Recommendation systems(RS).Although research on RS social media is increas-ing year by year,the comprehensive literature review and classification of this RS research is limited and needs to be improved.The previous method did notfind any user reviews within a time,so it gets poor accuracy and doesn’tfilter the irre-levant comments efficiently.The Recursive Neural Network-based Trust Recom-mender System(RNN-TRS)is proposed to overcome this method’s problem.So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately.Thefirst step is to collect the data based on the transactional reviews of social media.The second step is pre-processing using Imbalanced Col-laborative Filtering(ICF)to remove the null values from the dataset.Extract the features from the pre-processing step using the Maximum Support Grade Scale(MSGS)to extract the maximum number of scaling features in the dataset and grade the weights(length,count,etc.).In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax acti-vation function for calculating the average weights of the features.Finally,In the classification method,the Recursive Neural Network-based Trust Recommender System(RNN-TRS)for User reviews based on the Positive and negative scores is analysed by the system.The simulation results improve the predicting accuracy and reduce time complexity better than previous methods.

我国青少年的媒体使用、媒体信任与政治信任——基于一项全国性问卷调查的研究

建构政治信任是青少年政治社会化的重要目标,媒体则是塑造该群体政治信任的重要途径。通过对广东、湖北、甘肃三省六地近1400名在校中学生的问卷调查,考察媒体使用和媒体信任对青少年群体政治信任的影响。数据分析表明,青少年的媒体使用既直接促进政治信任,还通过媒体信任对政治信任产生间接影响。媒体使用对青少年的政治信任具有显著的正向促进作用,即青少年使用媒体获取时政信息越频繁,其对于政治行为者、政府、政党、政治制度和政治系统的信任程度就越高。官方媒体信任和非官方媒体信任都能显著促进青少年的政治信任,且两种媒体信任在媒体使用和政治信任之间发挥着重要的中介作用。从政治传播视角揭示了媒体使用对青少年政治信任的多重影响路径,彰显和证实了媒体信任在该群体政治信任培育中的重要价值。将中介变量媒体信任划分为官方媒体信任和非官方媒体信任两个维度,丰富和拓展了学界对媒体信任和政治信任之间关系的理解。

数字乡村建设与农民工就业质量提升

数字乡村建设一方面通过新业态新模式创造出更多更高质量的就业岗位,另一方面通过拓宽信息渠道促进农民工人力资本和社会资本积累,增强农民工在就业市场上的竞争力和议价能力,因而可以赋能农民工高质量就业。将2018年的县域数字乡村指数数据与中国家庭追踪调查(CFPS)数据进行匹配,从客观和主观两个维度综合评价农民工的就业质量,检验数字乡村建设对农民工就业质量的影响,结果表明:数字乡村建设显著提高了农民工就业质量,该结论在内生性处理和一系列稳健性检验中依然成立;数字乡村建设具有显著的信息改善效应,可以通过拓宽信息渠道、促进人岗匹配、增强社会信任等路径来提高农民工的就业质量;数字乡村建设对女性农民工、在婚农民工、中青年农民工、学历较高的农民工和已接入互联网的农民工具有更显著的就业质量提升作用。因此,应加快推进数字乡村建设,充分发挥其各种积极效应,并有效缩小城乡间和农村地区内部的数字鸿沟,促进数字乡村协调发展。

“合群”对民众主观幸福感影响的实证研究——基于中国社会状况综合调查数据的分析

运用2021年“中国社会状况综合调查数据”,在建立二元Logit模型基础上,使用非线性模型的中介效应检验-KHB方法对“合群”效应进行分解,探讨了“合群”与当前中国民众主观幸福感的关系,验证了“合群”对民众主观幸福感的影响,并进一步比较分析了其中介作用机制。研究发现:“合群”可以显著提升民众的主观幸福感;“合群”通过影响社会信任水平和社会联结程度,进而影响主观幸福感;社会信任水平和社会联结程度是“合群”影响主观幸福感的重要中介机制,其中介效应比例存在一定差异,社会联结程度的解释比例要高于社会信任水平。在此基础上,证实了“合群”对民众主观幸福感增进作用的存在,也为更好地认识“合群”如何有效地促进民众主观幸福感提供了微观证据。

基于虚假信息特征的社交媒体大学生用户感知信任研究

[目的/意义]探究社交媒体虚假信息特征对感知信任的影响,提升大学生的虚假信息识别能力。[方法/过程]通过扎根理论编码提取虚假信息一般特征,利用眼动实验和问卷调查获取数据分析其与感知信任的关系。[结果/结论]虚假信息一般包括内容、发布者、传播者、情感和形式5个核心特征33个子特征;大学生在阅读虚假信息时主要关注发布者、标题、内容和图片文字部分;内容特征、发布者特征、形式特征、情感特征显著影响大学生对虚假信息的感知信任,传播者特征的影响不显著;大学生对虚假信息的平均感知信任评级为2.95±1.340,虚假信息识别能力有待提高。

生态农产品品牌形象对消费者信任的影响——基于消费者社交网络参与度的调节效应

本文基于信号传递理论阐述了生态农产品品牌形象影响消费者信任的逻辑机理,并利用全国1113位有机牛奶消费者的微观调查数据,实证检验了生态农产品品牌形象对消费者信任的影响效应及其传导机制。结果表明:(1)品牌作为一种能够降低消费者选择成本的排他性品类符号,具有重要的信息传递功能,对于解决生态农产品市场的信息不对称问题有积极意义,但不同品牌的影响力和号召力不同,其原因在于品牌形象存在差异,品牌形象越好,消费者对产品信任度越高。(2)在品牌形象对消费者信任的影响关系中,消费者社交网络参与度发挥着一定程度的正向调节作用,品牌形象对社交网络参与度较高的消费者产品信任影响较为强烈。(3)品牌形象对消费者信任的影响存在一定的异质性,对于社会信任水平较低和生态认知水平较高的消费者而言,品牌形象对其产品信任的影响较为显著。

促进还是抑制:社会信任对绿色生活方式的影响机制

绿色生活方式转型对推动生态文明建设具有重要意义。基于价值—信念—规范理论和信任—信息—合作模型等,构建“社会信任—环境风险感知—环境关心—绿色生活方式”的理论分析框架,采用中国综合社会调查(CGSS)2021年数据,运用结构方程模型,探索社会信任对绿色生活方式的影响机制,分析环境风险感知和环境关心的中介效应。研究表明:(1)居民社会信任程度的提高,可以直接激发绿色生活方式意愿,从而间接促进绿色生活方式行为;(2)居民社会信任程度的提高,会降低环境风险感知,间接抑制绿色生活方式行为;(3)社会信任会通过降低环境风险感知而减少环境关心,再同时间接抑制绿色生活方式意愿和行为。社会信任对绿色生活方式的双重作用机制表明,社会信任可能是一把“双刃剑”:社会信任虽然能直接促进绿色生活方式的形成,推动绿色生活方式的转型,但是也可能通过降低环境风险感知和环境关心,抑制绿色生活方式的意愿和行为。因此,培育居民绿色生活方式可直接聚焦环境风险感知和环境关心,激发居民保护环境的内在情感和动力,以有效促进居民绿色生活方式的转型。

产妇社会信任和心理弹性对产后创伤后应激障碍影响的路径分析

目的分析产妇社会信任和心理弹性对产后创伤后应激障碍(PTSD)影响的路径。方法采用创伤后应激障碍症状清单-平民版(PCL-C)、社会信任量表(STS)及心理弹性量表(CD-RISC)对312例产后产妇进行问卷调查。结果产妇产后PCL-C得分为(26.82±10.23)分;STS得分为(26.55±5.22)分;CD-RISC得分为(91.56±25.56)分。产妇产后STS和CD-RISC评分与PCL-C评分呈负相关(r=-0.177、-0.153,P<0.01),而STS评分与CD-RISC评分呈正相关(r=0.446,P<0.01)。心理弹性在社会信任与产后PTSD间的中介效应不显著,社会信任在心理弹性与产后PTSD间的中介效应不显著。结论心理弹性、社会信任与产后PTSD之间两两直接相关,即心理弹性直接影响产后PTSD,社会信任直接影响产后PTSD,心理弹性与社会信任间相互影响。

论新时代教育公平的社会心态支持

在教育改革与发展所需要的完整、和谐的社会支持系统中,广大民众的社会心态支持不可或缺;推进新时代教育公平,亦离不开优良的教育公平心态,即民众在有关教育公平的价值观、认知、情绪、行为倾向等方面,能够秉持科学理性的价值观、积极平和的情绪情感,对国家为推进教育公平而制定、执行的政策充满信任,对我国教育事业公平发展与质量提升葆有信心。建设优良的教育公平心态是一项重要而紧迫的任务,需要舆论正确导向、赋权公众参与、尊重多元选择,从而凝聚广大民众对教育及教育公平议题的正确的价值共识,提升广大民众对国家教育事业公平发展的社会信任,重建良好心态秩序。

社会公平感对景区-社区冲突的影响机制研究——基于政府信任的中介效应

景区—社区冲突作为旅游地的负面事件,既制约景区高质量发展,又影响和谐社会建设和乡村振兴的实现,增强社会公平感是化解此类冲突的有效途径。为厘清其作用机制,以L风景名胜区为案例,从社区居民感知视角出发,运用结构方程模型,以政府信任为中介变量,探究社会公平感对景区—社区冲突的影响机理。研究发现,社会公平感能够有效降低景区—社区冲突倾向,其中以互动公平感缓解冲突倾向的效果最佳;政府信任也能够显著降低景区—社区冲突倾向,且影响程度高于社会公平感;政府信任在社会公平感和冲突倾向之间起部分中介作用。对此,景区管理方需通过明确旅游吸引物权、设计合规的旅游参与程序、增强与社区居民的人文沟通与情感关怀、改善公共基础设施服务水平等措施提升社区居民的分配公平感、程序公平感、互动公平感与政府信任,以有效化解景区—社区冲突。

论闲置水权收储的法律性质

为规范落实最严格水资源管理制度,界定因散见各地方水权规范性文件而缺乏明确边界的闲置水权收储行为的法律性质,将闲置水权收储的特点与常见行政行为,包括行政许可撤回、行政征收、行政处罚与行政协议解除的特点进行比较分析。提出基于闲置水权收储规范存在缺少信赖利益、补偿条款、负面评价与协议形式的特性,导致闲置水权程序无法归类于现有行政行为体系之中。指出应分别针对财产权社会性义务与行政协议解除的方向进行调整,在减轻其行政限制程度之余,明确闲置水权收储应作为终局手段,在其他催告手段穷尽之后再行实施。