Exploring Evolution of Public Opinions on Tianya Club Using Dynamic Topic Models

Online media have brought tremendous changes to civic life, public opinions, and government administration. Compared with traditional media, online media not only allow individuals to browse news and express their views more freely, but also accelerate the transmission of opinions and expand influence. As public opinions may arouse societal unrest, it is worth detecting the primary topics and uncovering the evolution trends of public opinions for societal administration. Various algorithms are developed to deal with the huge volume of unstructured online media data. In this study,dynamic topic model is employed to explore topic content evolution and prevalence evolution using the original posts published from 2013 to 2017 on the Tianya Zatan Board of Tianya Club, which is one of the most popular BBS in China. Based on semantic similarities, topics are grouped into three themes: Family life, societal affairs, and government administration. The evolution of topic prevalence and content are affected by emergent incidents. Topics on family life become popular, while themes"societal affairs" and "government administration" with bigger standard deviations are more likely to be influenced by emergent hot events. Content evolution represented by monthly pairwise distance matrix is very easy to find change points of topic content.     

Exploring on-line societal risk perception for harmonious society measurement

The nearly 30-year economic growth miracle brings the consequent tremendous poor-rich gap leading strong drives for social transformation in current China. Chinese top leaders have realized to increase the peoples＇ income, improve quality of life and construct a ＂harmonious society＂ as key missions especially in recent 10 years. How to measure a harmonious society is one important topic as different measures may lead to different development policies. This paper outlines over 10 indices relevant to measure a harmonious society. Some are global indicators, while some are contributed by domestic researchers and arouse debates. Most of those indicators require conducting surveys on social attitudes under micro levels, which is always time consuming with problem of data quality. As Internet technology advances provide ways to record and disseminate fresh community ideas and thoughts conveniently, detecting topics or emotions from on-line public opinions is becoming a trend or one supplement way to overcome those data acquisition problems. This paper discusses one approach to on-line societal risk perception using hot search words and BBS posts. Such a trial aims to provide another way to societal risk perception different from those in traditional socio psychology studies. Challenges are also indicated.     

网络话题活性模型的仿真与分析

热点话题发现是互联网舆论研究中的基础性问题之一,为此提出了话题活性模型和快速筛选算法。话题活性模型能够在话题形成讨论热点的早期将其从混合的帖子到达流中分离出来;快速筛选算法仅根据即时信息进行计算,不需要长期历史信息,且计算的时间复杂度仅为O(n),较好的解决了热点话题快速筛选问题。最后仿真和实证,证明了该方法的有效性。     

基于跨媒体数据的反腐多视角分析

腐败严重影响社会发展和民众信心,本文获取中纪委反腐通告、"天涯杂谈"新发帖和"百度热搜新闻词"三种不同来源的互联网文本语料从多个视角分析十八大以来的反腐成果.使用LDA话题模型探测不同语料中的反腐话题,并对语料的时空特性进行分析,通过官员履历构建"官员共职网络"探索腐败官员团伙并结合时间、级别和领域信息分析高级别官员的反腐策略.结果表明,腐败官员相关的不同语料的时空分布不同且涉及的腐败相关话题的重点不同,"官员共职网络"对于研究腐败官员的复杂关系具有重要意义.     

Mathematical Models of Self-Appraisal in Social Networks

In social networks where individuals discuss opinions on a sequence of topics, the selfconfidence an individual exercises in relation to one topic, as measured by the weighting given to their own opinion as against the opinion of all others, can vary in the light of the self-appraisal by the individual of their contribution to the previous topic. This observation gives rise to a type of model termed a De Groot-Friedkin model. This paper reviews a number of results concerning this model.These include the asymptotic behavior of the self-confidence(as the number of topics goes to infinity),the possible emergence of an autocrat or small cohort of leaders, the effect of changes in the weighting given to opinions of others(in the light for example of their perceived expertise in relation to a particular topic under discussion), and the inclusion in the model of individual behavioral characteristics such as humility, arrogance, etc. Such behavioral characteristics create new opportunities for autocrats to emerge.     

基于频繁词集聚类的微博新话题快速发现

新话题发现是进行舆情分析的基础和前提，新话题发现的一个关键环节是进行关键词的聚类分析.目前，大量的新话题来源于微博，但是将传统的聚类算法用于微 博新话题发现时，会产生特征向量的高维性和稀疏性问题，使得聚类结果非常不准确，而且收敛时间难以控制，进而影响舆情分析的可靠性和实时性.鉴此，本文提 出了频繁词集聚类FWSC （frequent words sets clustering）方法.实验结果表明，我们提出的方法能够快速有效地发现新话题.     

关于大陆"社会网络"研究的iView分析及知识视图

相对于期刊文章需经历较长的评审过程, 学术会议报告和论文能反映领域研究的最新成果, 考察某领域的旗舰会议并对其跟踪有助于较快地把握研究脉络. 简要介绍iView分析这一定性综合集成技术基本原理后, 将 iView分析技术应用于中国社会网研究学会的年会"社会网与关系管理研讨会" 这一社会网领域在中国的代表性会议, 探测当前研究概况、主要议题、主要研究人员等, 进而考察系列研讨会所反映的该领域研究的地域特性、学科特性、时代特性及研究趋势. 如此展示支持快速准确勾画研究主题的知识视图的定性研究中智能信息技术的应用.     

Project Management Supports (Requisitely) Holistic: Socially Responsible Action in Business Systems

This article emphasizes the role of projects and project management in assuring socially responsible acting of contemporary business systems, presenting some practical applications in the field of tourism. With arising number of projects in business systems and their growing strategic importance, the role of project management has radically been changing for the last two decades. Managing organizations, as a central topic among seven crucial topics in ISO 26000 on social responsibility, is strongly associated with managing projects. Projects have important influence on the established as well as on establishing ongoing operations and are important tools in assuring business systems’ strategic development, by exposing interdependences and supporting the requisitely holistic approach. This article tries to fill the gap we detected in literature on social responsibility where projects and project management seem not to have a proper role. Four different links and interdependencies between project management and systems theory (which ISO 26000 reinforces) are exposed: (1) systemic treatment of project management, (2) systematical treatment of projects as holistic works, (3) systemic treatment of projects as parts of BSs’ strategies, (4) systemic treatment of projects throughout their entire life cycle.     

A Causality Analysis of Societal Risk Perception and Stock Market Volatility in China

Modern China is undergoing a variety of social conflicts as the arrival of new era with the transformation of the principal contradiction. Then monitoring the society stable is a huge workload. Online societal risk perception is acquired by mapping on-line public concerns respectively into societal risk events including national security, economy & finance, public morals, daily life, social stability, government management, and resources & environment, and then provides one kind of measurement toward the society state. Obviously, stable and harmonious social situations are the basic guarantee for the healthy development of the stock market. Thus we concern whether the variations of the societal risk are related to stock market volatility. We study their relationships by two steps, first the relationships between search trends and societal risk perception; next the relationships between societal risk perception and stock volatility. The weekend and holiday effects in China stock market are taken into consideration. Three different econometric methods are explored to observe the impacts of variations of societal risk on Shanghai Composite Index and Shenzhen Composite Index. 3 major findings are addressed. Firstly, there exist causal relations between Baidu Index and societal risk perception. Secondly, the perception of finance & economy, social stability, and government management has distinguishing effects on the volatility of both Shanghai Composite Index and Shenzhen Composite Index. Thirdly, the weekend and holiday effects of societal risk perception on the stock market are verified. The research demonstrates that capturing societal risk based on on-line public concerns is feasible and meaningful.     

The distributed representation for societal risk classification toward BBS posts

The risk classification of BBS posts is important to the evaluation of societal risk level within a period. Using the posts collected from Tianya forum as the data source, the authors adopted the societal risk indicators from socio psychology, and conduct document-level multiple societal risk classification of BBS posts. To effectively capture the semantics and word order of documents, a shallow neural network as Paragraph Vector is applied to realize the distributed vector representations of the posts in the vector space. Based on the document vectors, the authors apply one classification method KNN to identify the societal risk category of the posts. The experimental results reveal that paragraph vector in document-level societal risk classification achieves much faster training speed and at least 10% improvements of F-measures than Bag-of-Words. Furthermore, the performance of paragraph vector is also superior to edit distance and Lucene-based search method. The present work is the first attempt of combining document embedding method with socio psychology research results to public opinions area.     

微博舆论场:突发事件舆情演化分析的新视角

微博舆论场逐渐成为了突发事件网络舆情的策源地,在舆情生成演化中扮演重要角色.本文以微博舆论场为研究视角,首先运用超网络建模理论,构建了集社交、信息、心理、观点四层子场为一体的微博舆论场超网络模型,并对各层子场内部以及子场间关系进行建模分析;然后提出了微博舆论场超网络模型的衡量指标,对微博舆论场"场强"进行了量化分析;最后使用社会计算和数据挖掘算法,定量分析了微博舆论场对新进入的无知者和感染者的作用过程,以及对新个体发生作用后,微博舆论场中舆情的演化.以期对突发事件舆情态势进行预测预警,为舆情干预治理提供理论依据,有效引导突发事件舆情良性发展.     

Topic-Feature Lattices Construction and Visualization for Dynamic Topic Number

The topic recognition for dynamic topic number can realize the dynamic update of super parameters, and obtain the probability distribution of dynamic topics in time dimension, which helps to clear the understanding and tracking of convection text data. However, the current topic recognition model tends to be based on a fixed number of topics K and lacks multi-granularity analysis of subject knowledge. Therefore, it is impossible to deeply perceive the dynamic change of the topic in the time series. By introducing a novel approach on the basis of Infinite Latent Dirichlet allocation model, a topic feature lattice under the dynamic topic number is constructed. In the model, documents, topics and vocabularies are jointly modeled to generate two probability distribution matrices: Documentstopics and topic-feature words. Afterwards, the association intensity is computed between the topic and its feature vocabulary to establish the topic formal context matrix. Finally, the topic feature is induced according to the formal concept analysis(FCA) theory. The topic feature lattice under dynamic topic number(TFL DTN) model is validated on the real dataset by comparing with the mainstream methods. Experiments show that this model is more in line with actual needs, and achieves better results in semi-automatic modeling of topic visualization analysis.     

新媒体对审计师意见决策影响研究——以新浪微博为例

近年来,网络舆情已经成为情报学、传播学等领域研究的热点主题,新媒体是推动网络舆情出现的主要力量.新媒体舆情的形成、演化、预警、治理策略的研究较多,但是新媒体信息对特定行业人群信息决策行为的作用机理及其影响程度的研究较少.本文以新浪微博为例,利用Probit、Ordered Probit等回归模型实证分析了新媒体形成的网络舆情对审计意见决策的影响及其作用机理,以期了解和掌握新媒体对特定行业人群信息决策行为的影响,为新媒体治理和监管提供借鉴和支持.研究结果表明,审计师同时关注了新媒体发布的原创信息和转载的已有信息;新媒体发布和传播的公司负面信息越多,审计师越有可能对上市公司的年报出具非标准审计意见;新媒体负面信息缩短了公司违规被监管部门处罚的时间期限,在一定程度上增加了审计师面临的诉讼风险预期.     

The Application of Cobb-Douglas Function in Forecasting the Duration of Internet Public Opinions Caused by the Failure of Public Policies

Gushes of Internet public opinions may trigger unexpected incidents that significantly affect social security and stability, especially for ones caused by the failure of public policies. Therefore, forecasting this kind of Internet public opinions is of great significance. The duration could be cited as one of the most direct indicators that can reflect the severity of a specific Internet public opinion case. Based on this background, this paper aims to find the factors that may affect the duration of Internet public opinions, and accordingly proposes a model that can accurately predict the duration before the release of public policies. Specifically, an index system including 8 factors by considering four dimensions, namely, object, environment, reality (offline), and the network (online), is established. In addition, based on the dataset containing 23 typical Internet public opinion cases caused by the failure of public policies, 9 prediction models are gained by applying the multivariate linear regression model, multivariate nonlinear regression model, and the Cobb-Douglas function.     

基于BERT-LDA模型的新冠肺炎疫情网络舆情演化仿真

构建大规模网络舆情演化仿真模型,对新冠疫情武汉重灾区与全国其他地区采取差异化的应急管理和舆情疏导具有指导价值.为实现主题细粒度的舆情情感演化仿真,将LDA(LatentDirichlet Allocation)主题模型与BERT(Bidirectional Encoder Representations from Tr...     

数据智能:趋势与挑战

随着大数据和人工智能的兴起,数据智能(data intelligence)逐渐成为学术界和产业界共同关注的焦点.数据智能具有显著的大数据驱动和应用场景牵引两大特征.其融合场景内外的多源异质大数据,利用大规模数据挖掘、机器学习和深度学习等预测性分析方法和技术,提取数据中蕴含的有价值的模式,并用于提升复杂实践活动中的管理与决策水平.本文指出了推动数据智能实现迭代发展的三维要素:数据、算法和场景,然后围绕这三大要素介绍了数据智能的前沿热点、发展趋势和存在挑战,特别对数据智能与管理学交叉的研究与应用问题进行了较为深入的探索.论文还尝试给出一些具有前瞻性的观点或评论,一来希望为有兴趣进入数据智能领域的读者提供指引,二来希望能够在管理同行中起到抛砖引玉之效.     

Topic Splitting: A Hierarchical Topic Model Based on Non-Negative Matrix Factorization

Hierarchical topic model has been widely applied in many real applications, because it can build a hierarchy on topics with guaranteeing of topics’ quality. Most of traditional methods build a hierarchy by adopting low-level topics as new features to construct high-level ones, which will often cause semantic confusion between low-level topics and high-level ones. To address the above problem, we propose a novel topic model named hierarchical sparse NMF with orthogonal constraint (HSOC), which is based on non-negative matrix factorization and builds topic hierarchy via splitting super-topics into sub-topics. In HSOC, we introduce global independence, local independence and information consistency to constraint the split topics. Extensive experimental results on real-world corpora show that the purposed model achieves comparable performance on topic quality and better performance on semantic feature representation of documents compared with baseline methods.     

我国资源卫星的社会效益及其CVM评价

应用条件价值法对我国资源卫星的社会效益进行了定量评价. 研究发现,民众认为我国资源卫星的社会效益主要体现在促进科学教育、社会生活改善和产业发展等几个方面,我国绝大部分民众在经济条件允许的情况下,愿意为资源卫星的社会效益支付费用. 通过对调研数据的分析,估算出我国民众对资源卫星社会效益的总支付意愿约为96.7亿元人民币/年.     

Morality materialized: Notes on public prevention as a policy system

Public prevention is discussed as a policy system that owes its coherence to a number of “constitutive principles” rather than to an institutional fabric of its own. Notions from Luhmann's autopoietic social systems theory are combined with some recent developments in discourse analysis to outline this discussion theoretically. As to the constitutive principles, first, it is argued that public prevention is negatively constituted by its divorce from the regular healthcare system. Second, it is demonstrated that the constitutive principles of the public prevention system itself stem from the basic insecurities of this type of social policy. Public prevention strategies always have to impose themselves on existing (social) policy fields, and, more importantly, they often intrude upon people's private lives. That is why they tend to locate responsibility “outside,” moralizing the environment and moralizing things.     

网络节点(集)重要性的一种度量指标

度量网络节点(集)的重要性一直是社会网络分析领域和系统科学研究领域的一个值得研究的问题。系统科学的研究方法用网络的连通性来反映系统某种功能的完整性，通过度量节点删除对网络连通的破坏程度来反映网络节点(集)的重要性．但目前这一思想并没有得到真正意义上的量化，如果不考虑连通分支的大小和形状，就不能很好地反映出不同节点或不同网络之间在结构和位置特性上的差异。本文用节点(集)被删除后形成的所有不连通节点对之间的距离(最短路)的倒教之和来反应节点删除对网络连通的破坏程度即所删节点(集)的重要性。这一工作使重要性等价于破坏性的思想得到了精细的量化。