反沉默螺旋效应下社交媒体谣言传播动力学研究

目前，社交媒体已经成为网络谣言滋生的温床，其庞大的用户规模，以及即时性、开放性等特点，使得谣言传播的广度和速度远远高于现实社会网络，从而对人们的生活和社会的稳定产生巨大的影响.为此，基于传染病动力学及"反沉默螺旋"理论，构建了考虑辟谣行为及网络结构的社交媒体谣言传播模型；并进一步分析了模型中无谣言平衡点和谣言存在平衡点的存在性和稳定性，计算了模型的基本再生数；利用MATLAB对模型中各参数变化对谣言传播的影响进行了仿真分析，并对部分参数进行了敏感性分析；利用实际案例对模型进行了进一步的分析与验证；最后，给出了控制社交媒体中谣言传播的策略建议.     

商户与用户间社交关系对渠道协调的影响研究

随着微信,微博等社交媒体的广泛使用,越来越多的企业已经将社交媒体作为一种渠道进行营销.与传统渠道不同,社交媒体零售商可以与用户进行互动,建立社交关系,进而影响渠道的需求.通过建立Stackelberg博弈模型,讨论了零售商与用户之间的社交关系强度对各方利润在协调前后的影响.研究发现,零售商之间的竞争程度和社交关系强度同时影响到渠道的协调策略.当零售商间的竞争程度较低时,制造商不会主动对渠道进行协调;协调后,较强或者较弱的社交关系强度会对制造商的利润产生负向影响,而对零售商的利润则会产生正向影响;当零售商之间的竞争程度以及社交关系强度满足特定关系时,可以使渠道各方利润在协调后同时得到提高.     

Social media research: A review

Social media is fundamentally changing the way people communicate,consume and collaborate.It provides companies a new platform to interact with their customers.In academia,there is a surge in research efforts on understanding its effects.This paper aims to provide a review of current status of social media research.We discuss the specific domains in which the impacts of social media have been examined.A brief review of applicable research methodologies and approaches is also provided.     

Multi-Period Optimal Capacity Strategy Based on Consumer Behavior Involved in Social Media

Social media and consumer behavior are increasingly important in business nowadays. As a new form of advertising, social media do facilitate the increase in demand and bring a challenge to manufactures. While researchers demonstrated that insufficient capacity generates the loss in the process of sales, an opposite conclusion has been obtained that the profit is larger in insufficient capacity.This study investigates this situation of a manufacturer. We develop a multi period model of insufficient capacity concerning with social media and consumer behavior. An calculation of the model indicates that a great change appears in the demand of each period. To ensure the maximum profit, the capacity of each period is computed. And the profit is almost 8 times larger than that we do not consider social media and consumer behavior. We discuss the implications of our findings for both theory and practice.     

基于复杂网络理论的在线社会网络分析

综述了近年来复杂网络理论在在线社会网络研究中取得的进展,重点包括在线交友网络、在线社区和在线社会媒体等3大类在线社会网络的实证研究.在线社会网络的拓扑性质,尤其是度同配指数,与现实社会网络存在不同之处.解释了在线社会网络中度异配性产生的根源,指出在线的和现实的社会网络的形成具有不同的底层机制.最后总结了在线社会网络的研究意义、理论价值、潜在的应用和未来的研究方向.     

“Towards Re-Inventing Psychohistory”:Predicting the Popularity of Tomorrow’s News from Yesterday’s Twitter and News Feeds

Rapid advances in machine learning combined with wide availability of online social media have created considerable research activity in predicting what might be the news of tomorrow based on an analysis of the past.In this work,we present a deep learning forecasting framework which is capable to predict tomorrow’s news topics on Twitter and news feeds based on yesterday’s content and topic-interaction features.The proposed framework starts by generating topics from words using word embeddings and K-means clustering.Then temporal topic-networks are constructed where two topics are linked if the same user has worked on both topics.Structural and dynamic metrics calculated from networks along with content features and past activity,are used as input of a long short-term memory(LSTM)model,which predicts the number of mentions of a specific topic on the subsequent day.Utilizing dependencies among topics,our experiments on two Twitter datasets and the HuffPost news dataset demonstrate that selecting a topic’s historical local neighbors in the topic-network as extra features greatly improves the prediction accuracy and outperforms existing baselines.     

结合社交媒体数据的城市交通事件可视分析方法

社交媒体中的文本内容可对交通量数据进行补充,为此提出一个交通事件可视分析方法。建立交通事件文本处理模型,提取事件的描述信息;基于图嵌入算法学习道路节点属性的向量表示,建立道路相似性模型;结合核密度模型建立交通事件发生概率预测模型;设计了一个交互式可视分析界面对于交通事件进行可视分析与探索。通过交通信息抽取、道路相似性度量以及交通事件交互预测等案例分析,验证了所提方法的有效性,可以辅助交通部门管理决策。     

Exploring the Linear and Nonlinear Causality Between Internet Big Data and Stock Markets

In the era of big data, stock markets are closely connected with Internet big data from diverse sources. This paper makes the first attempt to compare the linkage between stock markets and various Internet big data collected from search engines, public media and social media. To achieve this purpose, a big data-based causality testing framework is proposed with three steps, i.e., data crawling,data mining and causality testing. Taking the Shanghai Stock Exchange and Shenzhen Stock Exchange as targets for stock markets, web search data, news, and microblogs as samples of Internet big data, some interesting findings can be obtained. 1) There is a strong bi-directional, linear and nonlinear Granger causality between stock markets and investors' web search behaviors due to some similar trends and uncertain factors. 2) News sentiments from public media have Granger causality with stock markets in a bi-directional linear way, while microblog sentiments from social media have Granger causality with stock markets in a unidirectional linear way, running from stock markets to microblog sentiments.3) News sentiments can explain the changes in stock markets better than microblog sentiments due to their authority. The results of this paper might provide some valuable information for both stock market investors and modelers.     

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.     

A Cognitive Science Framework for the Analysis of Knowledge-Based Systems

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社会化媒体调查与传统模式调查间的测量等同性

问卷调查已成为非常重要的实证研究方式,社会化媒体为网络调查的实施提供了新的平台. 调查者可利用该平台,采用混合调查模式来收集数据,以弥补单一调查方式样本涵盖率低的缺陷. 论文采用验证性因子分析,研究了纸笔调查、网页调查、BBS调查、微博调查以及SNS调查间的测量等同性问题. 研究结果表明：五组调查存在相同的基本测量结构及因子载荷矩阵;除纸笔调查外,其余四种平台调查还满足因子协方差矩阵等同性;而微博调查与SNS调查则更是具备截距等同性.     

多层同步网络在舆情仿真研究中的应用

为统筹考虑线上社交网络与线下社交网络在舆情传播中的作用,首次提出了一种包含"媒体层-线上层-线下层"的多层同步网络模型,并在此基础上搭建了舆情仿真系统框架.基于对人类社交网络拓扑结构的已有研究成果,给出了多层同步网络的缺省设定.通过案例仿真,分析了线上网络与线下网络之间的相互影响,对比了舆情事件目击者数量在多媒体传播、自媒体传播这两种舆论传播模式中的作用差异.最后,讨论了多层同步网络模型的兼容性与可扩展性,并指出了该模型改进的方向.     

基于消费者信任关系的在线口碑信息传播模型

在社会化媒体环境下，研究信任关系对在线口碑传播过程的影响，有助于企业制定合理的营销策略控制口碑传播。以社会关系强度刻画消费者之间的信任度，考虑社会强化现象及消费者兴趣转移现象；以SIR传染病模型为基础，构建基于信任关系的连续时间马尔科夫在线口碑传播模型。通过数值实验，分析了不同网络结构下信任关系对在线口碑传播的影响。实验结果显示：信任度的提升可显著促进在线口碑传播，当社区存在交叠时，社区信任度的促进作用增强，而社会信任度作用则减弱；另外，相比于社区内部信任，提升陌生人之间的信任促进作用更强，尤其是当网络社区结构一般或较弱时，且此时高影响力用户可在控制口碑传播过程中发挥更大作用。     

微博信息扩散的空间分析

为揭示信息扩散的空间特征,利用新浪微博数据,研究了中国地级市间的微博信息扩散,并利用重力模型,研究了影响城际信息扩散的因素。研究表明,少数一二线城市呈现信息寡占型,主导微博内容的输出和扩散。对城际信息交互模型的分析发现,用户数在很大程度上影响了城际信息扩散,城市总GDP也可预测城际信息交互,空间距离则不再发挥作用,微博中的信息扩散打破了物理距离的限制。该研究揭示了线上社交网络与线下物理空间的映射关系以及社交媒体中信息的城际扩散特征,可为空间位置相关的信息发布和网络舆情监控提供借鉴。     

邻居效应下的复杂社会网络扩散

复杂社会网络中的扩散不仅受到大众媒体等外部因素影响,更与其社会关系网络中邻居个体的选择密切相关.文中用"邻居效应函数"刻画邻居对个体选择的影响机制,分析了外部因素与邻居效应共同影响下的扩散均衡及其影响因素.研究发现,邻居效应函数的凹凸性决定了扩散的均衡,多重均衡的存在会增加扩散的不确定性,邻居效应的强度和复杂社会网络的结构特征对均衡采用者比例有重要影响.管理者在制定决策时应该考虑网络结构与邻居间相互影响.     

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.     

大数据营销:综述与展望

随着追踪消费者足迹能力的增强,营销科学正经历着一场大数据的革命.为了了解大数据背景下消费者行为和营销战略的改变,此项研究结合近十余年大数据营销的有关文献,梳理了大数据营销的相关概念,类型及分析方法,并提炼出搜索、移动、口碑、数字化、APP和社会媒体等50个大数据营销的热门主题.在此基础之上,分别从互联网、社会网络、移动互联网、大数据和人工智能等四个阶段对大数据营销的研究进展进行回顾,最后围绕客户旅程,营销活动的量化评估和营销分析技术的开发三个方面对大数据营销的未来研究方向进行展望.     

A Coupled Dynamic Model of Brand Acceptance and Promotive Information Spreading

When people try to decide to buy or not to, they are often influenced by both their inherent opinions and the social marketing activities e.g. advertising, social news with strong point of view. Then people will make their final choice, or even convince other people to buy. After all, this is the brand acceptance formation process. Factually, the dynamics of brand acceptance is essentially an interwoven dynamics of endogenous opinion dynamics disturbed by an information diffusion process. To have a better understanding of the dynamics of brand acceptance, we propose and analyze a coupled agent-based dynamic model that combines the Majority-Rule-based Voter model in opinion dynamics with the SI Model for information spreading to analyze the dynamics of brand acceptance in social media. We focus on two important parameters in diffusion dynamics: the decayed transmission rate (β) and the diffusion frequency (f). When the system is stable, the order parameter of the system is the duration time (τ). In the absence of opinion interaction, the simulation results indicate that, when a brand tries to occupy a larger market share through social marketing approaches, it is always effective to let the opponent to be the propaganda target. While with the Majority-Rule-based Voter Model included, we observe that the opinion interaction could have a dual function, which shows that a brand holding a small market share in the first place needs to adopt diverse marketing approaches according to different marketing environment types.     

Conceptualizing Mining of Firm's Web Log Files

In this era of a data-driven society, useful data(Big Data) is often unintentionally ignored due to lack of convenient tools and expensive software. For example, web log files can be used to identify explicit information of browsing patterns when users access web sites. Some hidden information,however, cannot be directly derived from the log files. We may need external resources to discover more knowledge from browsing patterns. The purpose of this study is to investigate the application of web usage mining based on web log files. The outcome of this study sets further directions of this investigation on what and how implicit information embedded in log files can be efficiently and effectively extracted. Further work involves combining the use of social media data to improve business decision quality.     

科普教育及媒体报道对于不实信息传播的影响

本文研究突发事件发生后不实信息的传播与扩散问题,首先,基于系统动力学的思想,提出了不实信息的动态传播模型,刻画了科普教育以及媒体报道对于不实信息传播的影响,分析了模型的稳定性态. 为了克服静态决策的局限性,论文基于最优控制理论的方法,构建了社会效用最大化的控制模型,利用庞特里亚金最大值原理,进一步探讨得出了不实信息传播的动态最优控制策略. 最后,基于模型推导结论和数据模拟,说明了最优控制的优势所在,提出了在应急管理中不实信息的控制建议与思考,为应急管理奠定了理论基础和决策依据.