Topics and trends of the on-line public concerns based on Tianya forum

Many social events spread fast through the Internet and arouse wide community discussions. Those on-line public opinions emerge into diverse topics along the time. Moreover, the strength of the topics is fluctuating. How to catch both primary topics and trend of topics over the shifting on-line discussions are not only of theoretical importance for scientific research, but also of practical importance for societal management especially in current China. To try the cutting-edge text analytic technologies to deal with unstructured on-line public opinions and provide support for social problem-solving in the big data era is worth an endeavour. This paper applies dynamic topic model （DTM） to explore the changing topics of new posts collected from Tianya Zatan Board of Tianya Club, the most influential Chinese BBS in mainland China. By analysis of the hot and cold terms trends, we catch the topics shift of main on-line concerns with illustrations of topics of school bus and environment in December of 2011. An algorithm is proposed to compute the strength fluctuation of each topic. With visualized analysis of the respective main topics in several months of 2012, some patterns of the topics fluctuation on the board are summarized.     

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.     

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。     

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.     

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.     

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...     

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.     

Ensemble of multiple kNN classifiers for societal risk classification

Societal risk classification is a fundamental and complex issue for societal risk perception. To conduct societal risk classification, Tianya Forum posts are selected as the data source, and four kinds of representations: string representation, term-frequency representation, TF-IDF representation and the distributed representation of BBS posts are applied. Using edit distance or cosine similarity as distance metric, four k-Nearest Neighbor (kNN) classifiers based on different representations are developed and compared. Owing to the priority of word order and semantic extraction of the neural network model Paragraph Vector, kNN based on the distributed representation generated by Paragraph Vector (kNN-PV) shows effectiveness for societal risk classification. Furthermore, to improve the performance of societal risk classification, through different weights, kNN-PV is combined with other three kNN classifiers as an ensemble model. Through brute force grid search method, the optimal weights are assigned to different kNN classifiers. Compared with kNN-PV, the experimental results reveal that Macro-F of the ensemble method is significantly improved for societal risk classification.     

基于公众认知与政府引导的邻避舆情演化模型

针对近年邻避群体事件的舆情诱发性,基于公众认知和政府引导视角研究邻避舆情演化问题.首先,通过多案例比较分析,提炼邻避舆情演化的宏观特征,并基于Gompertz模型改进给予刻画;随后,分析个体对邻避项目的微观认知交互行为,运用演化博弈建模来描述个体复制动态;同时,考虑政府通过正面舆论对邻避舆情给予回应,运用信息熵理论和动力学方法构建政府舆论引导的交互熵模型和动力学方程;进而在此基础上,给出"公众-政府"邻避舆情演化模型;最后,讨论该模型均衡点的存在性和稳定性,并以"广东茂名PX事件"的邻避舆情演化为基础案例进行数值仿真研究.结果表明:当公众对邻避项目风险认知偏差较小时,政府应适当尊重邻避舆情,并遵循渐进式的引导路径对邻避舆情给予及时、主动的关切性回应,否则,邻避舆情会愈演愈烈;而当公众对邻避项目风险认知偏差较大时,政府应对邻避舆情加强跟踪和监管,并给予及时、适量的客观回应,否则,邻避舆情会演化到较高稳态.     

基于模型集成的突发事件舆情分析与趋势预测研究

近年来,突发事件发生后,事件演进受网络舆情的影响越来越大,分析突发事件发生后网民情绪并进行预测,可为政府部门的应急管理和策略设计提供有效支撑,赢得宝贵的时间.本文提出了一个基于模型集成的微博情感分析与预测模型,对突发事件微博舆情进行情感分类与趋势预测.为了更准确地分析微博情感与未来走势,首先,利用多模型集成策略对突发事件相关的单条微博进行情感分析;接着,将单条微博情感进行集成,形成微博情感时间序列;再次,利用多模型集成思路对微博情感的未来走势进行预测;最后,通过实例验证提出方法的有效性.实证结果表明,集成模型较传统分类在微博情感分析上具有优势,集成模型较传统回归模型在微博情感走势预测同样具有明显优势,可以取得较高的预测精度.     

企业社会责任负面事件网络舆情演化与政企合作研究

企业社会责任负面事件网络舆情演化具有主体行为复杂性,是动态的复杂网络系统,把握舆情演化过程中主体的行为特征和互动关系有助于舆情管理.本文以企业、政府、民众、媒体和意见领袖五个主体为节点,以不同主体之间交互关系为路径,构建了企业社会责任负面事件网络舆情演化的多主体模型.选取"百度魏则西"为基础事件,采用系统动力学理论和方法,探究了企业社会责任负面事件网络舆情的演化过程和系统结构中的动力学行为.研究结果表明:企业社会责任负面事件网络舆情演化总体呈现了诱发、扩散和消退三个阶段的周期性过程.企业与利益相关者之间的交互作用是推动舆情演化的主要动因,其中企业和政府作为两个重要的行为主体,在联动的时点选择上,按照政府调控行为在先、企业应对行为在后的方式,能有效发挥政府的调控作用和企业的主观能动性,缓解舆论危机.     

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.     

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.     

Business Action Research in Practice—A Strategic Conversation About Conducting Action Research in Business Organizations

The objectiveof this paper is to provide practitioner researchers with insights into the initial findings around the challenges of conducting business action research in practice in commercial settings on the basis of experiences of a PhD cohort at Monash University in the first 18 months of candidature. In performing the role of a concluding paper, it sets out a generic framework for action research that the cohort has come to embrace. In doing so, it draws on emergent themes spread across the six diverse topics that are the subject of action research interventions of the cohort members. The paper then identifies and analyses the common patterns that have emerged and offers observations and conclusions for those involved in practitioner research.     

什么导致了股票收益的“媒体效应”:预期关注还是未预期关注?

本文借助百度新闻搜索引擎获取上市公司的网络新闻数据,利用中国A股市场2003年到2010年的股票收益数据进行检验,实证结果支持Fang等所发现的"媒体效应".为了揭示媒体关注度定价效应的内在机理,本文对媒体关注度进行分解,发现股票收益的"媒体效应"主要来自于媒体关注度的未预期部分,而未预期关注度的定价效应很大程度上可以由Miller的"基于卖空约束的异质信念"假说以及本文所提出的"投机性关注"假说所解释.     

Generating Storyline with Societal Risk from Tianya Club

Major societal problems affect the social stability. It is necessary to understand the public opinion toward those issues to avoid social conflicts. Nowadays the social media become the major platform to track what the public is concerned about and which may be of the societal risk. However,it is very tough to capture the public attention in short time due to huge flow of user-generated contents.In this paper, we approach this problem by expanding the method of generating storyline with the result displayed by a multi-view graph. One real-world example is illustrated and evaluation is given to show the effectiveness of the proposed method.     

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.     

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.     

基于系统动力学的危化品水污染突发事件中网络舆情危机应急策略研究

鉴于危化品强腐蚀性、剧毒等特性,其突发泄露事件,特别是水污染突发事件,将对人民生命安全和财产安全造成严重威胁,从而引发网络舆情危机.本文基于系统动力学,深入分析危化品水污染突发事件中网络舆情的扩散演化规律,以探索有效的危机应急策略.新模型包括网民子块、网媒子块和政府子块三个子模块,受到危化品水污染突发事件的直接影响,并影响着突发事件的发展演化.以五个危化品水污染突发事件为案例,实证研究验证并分析了仿真模型的有效性和实用性;在此基础上,对不同危机应急策略进行了模拟.政策仿真结果表明:提高官方新闻透明度、政府危机处理力度、政府响应效率能有效控制并引导网络舆情的扩散与传播.