外倾性对社交网络结构与关系的影响研究

发布时间：2018-07-10 02:12

本文选题：外倾性 + 网络规模　；参考：《北京邮电大学》2017年硕士论文

【摘要】：随着线上社交网络的快速发展和广泛普及,基于社交网络的用户心理探究和精准营销模式分析逐渐成为跨学科研究关注的热点。学术界越来越多的学者将目光放在用户心理对社交网络特征的影响研究上,从而进一步寻找针对不同用户分群的精准推荐和营销模式。对线上社交网络的相关研究不应该忽略影响其网络特征形成的心理因素,因此,本文重点关注个体个性特质对社交网络特征的影响,进一步完善相关研究,主要内容和成果如下:1、梳理个性特质对社交网络结构与关系影响的国内外文献与研究脉络,同时构建外倾性对社交网络特征影响作用的研究模型。本文梳理了大五人格和社交网络分析领域的相关文献与研究演进路径,归纳总结了大五人格对社交网络特征影响的模型和方法,为本文的研究打下坚实的基础。本文以大五人格中的外倾性为自变量,研究了个体外倾性的差异对其所在自我中心网络中结构维度变量(网络中心性)和关系维度变量 (关系规模、互动频率)的影响,通过构建影响模型,探索变量之间的关系。2、通过实证研究探索外倾性对社交网络结构与关系的影响,同时验证了社交网络结构维度与关系维度之间的关系。在综述了前人研究大五人格与社交网络特征之间关系的文献后,本研究以新浪微博为例,针对外倾性对社交网络特征的影响和社交网络特征间的关系提出了四个假设,创新性的探究线上社交网络与用户心理之间以及社交网络不同维度指标间的关系。采取发放调查问卷和平台数据抓取的形式进行数据收集,共发放问卷232份,回收有效问卷211份。后借助SPSS23.0和Smart PLS2.0对研究数据进行描述性统计分析和推断性统计分析。数据分析结果表明:(1)外倾性对社交网络结构维度指标——网络中心性和社交网络关系维度指标——关系规模和互动频率有显著正向影响。外倾性高的个体在新浪微博上更容易表现为互粉率高,互动频繁,社交圈规模大。(2)社交网络结构指标——网络中心性正向影响社交网络关系维度指标——互动频率。个体在社交网络中占据中心位置时,更容易被网络中的其他人信任,他人更愿意与该个体互动。3、通过分析个体个性特质对社交网络特征的影响进一步细分用户群,针对不同用户群进行精准推荐与社交网络营销策略建议。在上述研究结论的基础上,从企业提升服务价值和商业价值两个目的出发,给出了以客户体验为重心,制定适合用户使用习惯的个性化推荐信息流;站在企业商业化角度,深挖企业拥有的数据价值,为自己提供数据变现通道,形成业务生态链,实现多方共赢;加强与其他企业合作,提供有价值的数据,实现大数据变现,最大概度的发挥企业拥有的用户数据的价值等管理建议。
[Abstract]:With the rapid development and popularization of online social networks, the research of user psychology and accurate marketing model based on social networks has gradually become the focus of interdisciplinary research. More and more scholars focus on the impact of user psychology on the characteristics of social networks, so as to further explore the accurate recommendation and marketing model for different user groups. The related research on the online social network should not ignore the psychological factors that affect the formation of its network characteristics. Therefore, this paper focuses on the influence of individual personality traits on the social network characteristics, and further improves the relevant research. The main contents and results are as follows: 1, combing the domestic and foreign literature and research context of the influence of personality traits on the social network structure and relationship, and constructing the research model of the influence of extroversion on the social network characteristics. This paper combs the related literature and the research evolution path in the field of Big five Personality and Social Network Analysis, summarizes the model and method of the influence of Big five Personality on the characteristics of Social Network, and lays a solid foundation for the research of this paper. Taking extroversion in Big five personality as independent variable, this paper studies the influence of individual extroversion on structural dimension variable (network centrality) and relational dimension variable (relationship scale, interaction frequency) in their egocentric network. By constructing the influence model, exploring the relationship between variables. 2. Through empirical research to explore the influence of extroversion on the social network structure and relationship, and to verify the relationship between the social network dimension and the relationship dimension. After summarizing the previous literatures on the relationship between Big five Personality and Social Network characteristics, this study, taking Sina Weibo as an example, puts forward four hypotheses about the influence of extroversion on social network features and the relationship between them. This paper explores the relationship between social network and user psychology and the different dimensions of social network. 232 questionnaires were distributed and 211 valid questionnaires were collected. Then the descriptive statistical analysis and inferential statistical analysis were carried out with SPSS 23.0 and Smart PLS2.0. The results of data analysis show that: (1) extroversion has a significant positive effect on the relationship scale and frequency of social network structure dimension index-network centrality and social network relationship dimension. Individuals with high extroversion are more likely to exhibit high cross-pollination rate, frequent interaction and large scale of social circle on Sina Weibo. (2) the index of social network structure-the positive influence of network centrality on social network relationship dimension index-interaction frequency. When an individual occupies a central position in a social network, he is more likely to be trusted by other people in the network, and others are more willing to interact with the individual. 3. By analyzing the influence of individual personality traits on the characteristics of social network, the user group is further subdivided. For different groups of users for accurate recommendation and social network marketing strategy recommendations. On the basis of the above conclusions, from the two purposes of promoting the service value and the commercial value of the enterprise, this paper puts forward the individualized recommendation information flow, which is based on the customer experience, which is suitable for the user's usage habits, and stands at the angle of the enterprise commercialization. Deeply excavate the data value that the enterprise has, provide the channel of data realization for oneself, form the business ecological chain, realize the multi-win, strengthen the cooperation with other enterprises, provide valuable data, realize the realization of big data, The most general use of the value of user data owned by the enterprise and other management recommendations.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F49

【参考文献】