基于社交媒体的关联性用户属性推断

挖掘用户属性对用户建模、用户检索和个性化服务等具有十分重要的意义.已有的相关研究工作都是单独挖掘各种属性,而且忽略了各属性之间的相关关系.提出一种基于超图学习的用户属性推断的方法.在超图中,顶点表示社会媒体中的用户,超边表示用户产生的内容相似性与属性之间的关系.在建好的超图模型上,把用户属性挖掘形式化成一个正则化的标签相似传播问题,可以有效推断得到用户的各种属性.利用从Google+上收集的标记过全部属性的数据集进行了大量的实验,其结果表明了该方法在用户属性挖掘中的有效性.     

贝叶斯网络推理的研究与分析

贝叶斯网络是人工智能中不确定知识表示和推理的有力工具.介绍了贝叶斯网络的概念,给出一个实例,分析了贝叶斯网络推理的方法和过程.     

贝叶斯网络推理的研究与分析

贝叶斯网络是人工智能中不确定知识表示和推理的有力工具。介绍了贝叶斯网络的概念,给出一个实例,分析了贝叶斯网络推理的方法和过程。     

融合社交情感分析的股市预测探究

针对现有股市预测研究中所存在的大众情感度量不够全面的问题,提出了一种基于社交情感分析的股市预测模型. 该模型首先基于异构图模型的证券情感量化方法对社交媒介的数据进行情感分析,得到量化的情感时间序列;然后,基于自组织神经网络模型对情感序列及行情指数序列进行建模,从而对股票指数进行预测. 在国内社交媒介及股市行情数据集上的实验结果表明,本文所建立的模型在预测误差和精度上较BP（Back Propagation）神经网络分别提升了15%和12%,能更好地预测股票指数.     

Social Media Use for News and Individuals' Social Capital,Civic Engagement and Political Participation

Recently, scholars tested how digital media use for informational purposes similarly contributes to foster democratic processes and the creation of social capital. Nevertheless, in the context of today's socially‐networked‐society and the rise of social media applications (i.e., Facebook) new perspectives need to be considered. Based on U.S. national data, results show that after controlling for demographic variables, traditional media use offline and online, political constructs (knowledge and efficacy), and frequency and size of political discussion networks, seeking information via social network sites is a positive and significant predictor of people's social capital and civic and political participatory behaviors, online and offline.     

A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments.However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified.In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models.We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979-2002) for estimating the effect of maternal smoking during pregnancy on the development of the child's cognitive functioning.     

News Recommendations from Social Media Opinion Leaders: Effects on Media Trust and Information Seeking

Polls show a strong decline in public trust of traditional news outlets; however, social media offers new avenues for receiving news content. This experiment used the Facebook API to manipulate whether a news story appeared to have been posted on Facebook by one of the respondent's real‐life Facebook friends. Results show that social media recommendations improve levels of media trust, and also make people want to follow more news from that particular media outlet in the future. Moreover, these effects are amplified when the real‐life friend sharing the story on social media is perceived as an opinion leader. Implications for democracy and the news business are discussed.     

互联网股票新闻归类和板块分析的方法

从互联网角度研究和分析了股市新闻。针对互联网股市新闻的特点,提出了对它们进行分类分析的方法,通过对股市新闻的分析,期望能够找到股市运行的一些规律。在按照个股特征对网络上的股市新闻条目分类的同时,利用自然语言处理的功能,深入分析股市新闻内容,得到该股市新闻涉及的其他个股以及板块信息,并总结其影响力。     

基于独立成份分析的证券市场分析

为研究独立成分分析用于证券市场分析,首先介绍了独立成分分析的理论基础,然后介绍了一种独立成分分析的具体算法Fastica算法。为分析出对股票成交量影响的因素,进行了几只重点股票交易额的ICA分离实验,并对实验结果进行了分析研究,从而找到了影响股票交易量的几个重要因素,揭示出了股票交易额变化的几个重要原因。结果说明ICA是揭示隐藏在想象背后的原因的有效手段,非常适合于对证券市场的分析。     

Internet,News, and Political Trust: The Difference Between Social Media and Online Media Outlets

What is the relationship between Internet usage and political trust? To answer this question, we performed a cross‐sectional analysis of Eurobarometer survey data related to 27 countries and a supervised sentiment analysis of online political information broadcast during the Italian debate on the reform of public funding of parties. The results disclose the differences between Web 1.0 websites and Web 2.0 social media, showing that consumption of news from information/news websites is positively associated with higher trust, while access to information available on social media is linked with lower trust. This has implications for the debate on social media as a public sphere and for the tension between professional and citizen journalism.     

社交媒体数据的获取分析

本文主要介绍了对于社交网络中媒体数据的获取分析。首先介绍了一般的获取策略,即获取数据的一般过程。然后根据当前情况,主要是对两种方式进行分别分析。并在两种方式上分别提出了提高效率的一般方法。最后对这两种获取方式进行性能比较,并做出总结。     

独立成分分析法及其在股票分析中的应用

从信息论和最优化的角度阐述了用于信号分离、特征提取以及金融数据分析的独立成分分析法,并结合中国股票市场的特点用独立成分分析法对各阶段股票走势和收益进行了分析和预测。     

Undirected and Directed Network Analysis of the Chinese Stock Market

Computational Economics - To study the characteristics of Chinese stock market, this paper analyses the undirected and directed stock market networks of the constituent stocks of CSI 300. We first...     

基于复杂网络的证券市场智能建模与分析

股票市场作为金融系统的重要组成部分,是一个典型的具有结构复杂性和节点复杂性的复杂网络系统.作为拆分和了解复杂网络的有力工具,社团结构分析被广泛应用于社交网络、物流网络等多种复杂网络系统,并取得了突破性成果.论文采用Pearson相关系数来度量中国A股市场中股票价格波动的相关关系,构建股票市场加权网络,利用改进型社团相似性指标,选定了股票市场时序动态加权网络的步长与社团划分算法,并对社团结构进行了简要分析.     

基于复杂网络的证券市场智能建模与分析

股票市场作为金融系统的重要组成部分,是一个典型的具有结构复杂性和节点复杂性的复杂网络系统.作为拆分和了解复杂网络的有力工具,社团结构分析被广泛应用于社交网络、物流网络等多种复杂网络系统,并取得了突破性成果.论文采用Pearson相关系数来度量中国A股市场中股票价格波动的相关关系,构建股票市场加权网络,利用改进型社团相似性指标,选定了股票市场时序动态加权网络的步长与社团划分算法,并对社团结构进行了简要分析.     

基于股市网络中心性的投资组合构建与分析

分别采用归一化互信息和Pearson相关系数两种指标衡量A股市场股票价格波动相关性,并利用阈值法和PM-FG算法来构建股票市场网络.通过对所构建网络模型的比较和分析,发现基于Pearson相关系数的阈值筛选法表现较优.基于所构建的A股市场时序动态网络,对度中心性进行研究,通过设计度中心性策略并进行实证性分析,得到了市场表现更好的基于社团分析的度中心性策略.     

基于社会媒体的旅游数据挖掘与分析

信息技术和社会媒体的快速发展使得网上旅游数据日益庞大。随着游客对旅游需求呈现多样化和定制化趋势,旅游信息服务成为目前的研究热点。社会媒体中的社区和自媒体平台存在着海量的旅游信息资源,基于社会媒体的旅游数据挖掘可 以充分利用这些资源,将其应用在智慧旅游推荐上,进而推动“互联网+旅游信息化”的快速发展。本文全面分析和讨论了目前“互联网+旅游信息化”的研究背景和发展历程,分析了目前社会媒体中的旅游数据的特点,进一步介绍了“互联网+旅游信息化”背景下的热点研究应用,最后总结了基于社会媒体旅游数据的挖掘与应用的研究难点,并且对未来可能的研究方向 进行了探讨。     

流媒体技术分析及其在新闻网站的应用

随着互联网的普及和多媒体技术的发展,流媒体依靠其强大的技术优势迅速渗透到社会生活的各个领域。在国际上,对webcasting（或webcaster）的研究已经成为网络传播研究的一项重要课题。本文以流媒体的概念为切入点,深入分析了流媒体系统及其关键技术,研究了流媒体技术在新闻网站中的应用。     

Bayesian Analysis of Student t Linear Regression with Unknown Change-Point and Application to Stock Data Analysis

This article devotes to studying the variance change-points problem in student t linear regression models. By exploiting the equivalence of the student t distribution and an appropriate scale mixture of normal distributions, a Bayesian approach combined with Gibbs sampling is developed to detect the single and multiple change points. Some simulation studies are performed to display the process of the detection and investigate the effects of the developed approach. Finally, for illustration, the Dow Jones index closed data of U.S. market are analyzed and three variance change-points are detected.     

Calling Out Fake News on Social Media: A Comparison of Literature in Librarianship and Journalism

This study compared 475 scholarly journal articles in librarianship and journalism on social media published between 2006 and early 2017. Though articles from these two information professions are mostly positive on social media, various journalism articles pointed out the information credibility issue on social media while no librarianship article did that. With fake news being a major issue in the information business since the 2016?US election, librarians should do more in calling out fake news on social media in their publications as well as in information literacy classrooms.