水利舆情大数据监测应用研究

随着社交媒体的发展,水利网络舆情事件的发生次数呈上升趋势,监测分析难度日益加大.为解决水利网络舆情监测问题,开发建设水利舆情大数据监测分析平台十分必要.通过对基础设施、数据资源、舆情智能分析、舆情大数据可视化4个模块的构建,水利舆情大数据监测分析平台初步实现舆情大数据监测分析能力,形成较完善的水利行业舆情大数据工作机制...     

Regional Sentiment Bias in Social Media Reporting During Crises

Crisis events such as terrorist attacks are extensively commented upon on social media platforms such as Twitter. For this reason, social media content posted during emergency events is increasingly being used by news media and in social studies to characterize the public’s reaction to those events. This is typically achieved by having journalists select ‘representative’ tweets to show, or a classifier trained on prior human-annotated tweets is used to provide a sentiment/emotion breakdown for the event. However, social media users, journalists and annotators do not exist in isolation, they each have their own context and world view. In this paper, we ask the question, ‘to what extent do local and international biases affect the sentiments expressed on social media and the way that social media content is interpreted by annotators’. In particular, we perform a multi-lingual study spanning two events and three languages. We show that there are marked disparities between the emotions expressed by users in different languages for an event. For instance, during the 2016 Paris attack, there was 16% more negative comments written in the English than written in French, even though the event originated on French soil. Furthermore, we observed that sentiment biases also affect annotators from those regions, which can negatively impact the accuracy of social media labelling efforts. This highlights the need to consider the sentiment biases of users in different countries, both when analysing events through the lens of social media, but also when using social media as a data source, and for training automatic classification models.     

互联网舆情监管与应对技术探究

当前,随着互联网的技术性、自由性、互动性、普及性的发展,互联网正成为社会各种利益诉求的表达、意识形态较量的媒介,成为社会公众关注时事政治、评论时事政治的公共平台,成为快速表达民意的渠道。这就要求有关部门要对网络信息内容实施有效监管,以保证社会的安全与稳定,而要实现对海量的网上内容的有效监管,就必须得到网络舆情监管技术的支撑。文章介绍和分析了中国网络舆情发展的现状和网络内容监管技术的发展方向,对网络舆情采集与获取技术、舆情处理技术进行了集中分析。     

基于Hadoop的微博舆情监控系统

随着在线社会网络如社交网站、微博、在线社区等的快速发展, 一个真正的双向传播和新媒体时代逐步形成. 在线社会网络让每个用户都能创造自己的内容, 并且快速传播出去. 据不完全统计, 新浪微博平均每秒有超过1000条的新微博产生, 日增量数据为5TB, 因此海量数据给舆情监控带来了严峻的挑战. 将介绍一种基于Hadoop的微博舆情监控系统, 能够对大规模采集数据进行挖掘、分析, 实现对舆情热点话题的发现及追踪、对微博的社会网络分析, 分析结果可视化呈现, 为党政机关、大型企业等单位和组织及时发现敏感信息、掌握     

一种搜索引擎日志分析的网络舆情监测探究

近几年来,随着互联网信息技术的不断发展,电脑已得到广泛普及.而网民逐渐开始应用网络论坛、博客、微博纷纷对社会事务发表看法,由此而形成网络舆情,已成为社会舆情的重要表现形式.由于网络舆情具有传播速度快、范围广且突发的特点,加强其监测已受到政府部门的重视.本文特提出一种搜索引擎日志分析的网络舆情监测方法,以望对后期网络舆情监测工作提供参考借鉴.     

基于深度神经网络的微博文本情感倾向性分析

随着新型社交媒体的发展,作为传播网络舆论的重要媒介,微博已然成为挖掘民意的平台.自然语言处理技术可以从微博文本中提取有效情感信息,为网络舆情监控、预测潜在问题及产品分析等提供科学的决策依据.为了克服现有的浅层学习算法对复杂函数表示能力有限的问题,本文尝试融合深度学习的思想,提出基于Word2Vec和针对长短时记忆网络改进的循环神经网络的方法进行中文微博情感分析.在两万多条中文标注语料上进行训练实验,实验数据与SVM、RNN、CNN作对比,对比结果证明,本文提出的情感分析模型准确率达到了91.96%,可以有效提高微博文本情感分类的正确率.     

互联网舆情监测、预警和引导技术

互联网舆情作为网络空间认知域的重要研究方向,已成为影响社会持续有序发展、维护国家安全和稳定的重要因素。在对现阶段舆情信息监测技术进行深入研究的基础上,分析了国内技术水平的差距,结合互联网空间认知域的分级预警和信息引导技术,提出了一个完整的舆情管控体系,融合了舆情的监测、预警和引导手段,达到化解公共危机和挫败恐怖分子破坏社会安定的目的。     

Internet Authorship: Social and Political Implications Within Kyrgyzstan

This paper presents the results of our corroborated study of grassroots Internet sites and authors in the nation of Kyrgyzstan, exceptional in Central Asia for its deregulated Internet policy. The study presents a set of semistructured interviews with notable grassroots Internet authors and activists, including bloggers, forum participants, and journalists, and analyzes this data via a critical communication and media studies lens to point to significant implications on emergent social, cultural, and political movements in the nation.     

基于互联网的数字媒体内容舆情分析系统

快速发展的互联网已成为反应社会舆情的重要载体之一,如何对互联网数字媒体内容舆情进行分析监控,及时有效地发现危害社会安全的各类有害虚假的舆情信息,已成为促进我国数字媒体内容舆情信息安全和内容监管健康发展迫切需要解决的问题。提出了一个基于互联网的数字媒体内容舆情分析模型,设计并实现了基于互联网的数字媒体内容舆情分析系统,并就涉及的实用关键技术进行了探讨。     

藏文舆情云分析系统平台研究

文章介绍了藏文舆情云分析平台的系统框架和核心模块,系统运用云计算技术在用户不增加高性能设备的前提下,实现由完全不懂藏语言文字的用户对纸质的藏文印刷体文本、存储介质获取的藏文各类格式文档,以及互联网等多种来源实施舆情监控,采用最大熵与条件随机场结合的方法进行命名实体识别,通过词语级以及句子级的辅助翻译技术将藏文舆情分析结果用中文呈报给用户。     

The tweets that killed a university: A case study investigating the use of traditional and social media in the closure of a state university

The 12-month discussion surrounding a regional university campus quickly evolved from a suggestion of independence, to a plan, to the ultimate closure of the university. This unique series of events at the University of South Florida Polytechnic (USFP) allows for an investigation of how various forms of media were used during this significant event that impacted college student’s education and immediate future. A campus wide survey was combined with social and online media monitoring to assess the topics, authors, and methods used during prominent discussions during and preceding the closure of USFP. Although social media played a crucial role, the most common format was Twitter and it was used almost exclusively by members of the media itself. Students instead relied on traditional sources to gather information. Additionally, students expressed their opinion utilizing classic methods, such as petitions, foregoing more modern Twitter or Facebook campaigns. It is incorrect to automatically assume younger demographic authorship or utilization of social media technology. Whereas social media use could expand even more over the next decade, identifying authorship remains critical as it is unclear how frequent social media is viewed as an official method of public discussion, especially when politics and higher education collide.     

A public and large-scale expert information fusion method and its application: Mining public opinion via sentiment analysis and measuring public dynamic reliability

With the rapid development of social media, reliable information released by the public on social media can provide important decision-making support. Therefore, the consideration of the public as another decision-making body participating in large-scale group decision-making (LSGDM) problems has become an extensively researched topic. However, the participation of the public as a decision-making body with decision-making experts faces several issues, such as the acquisition of public opinion, the reliability of public opinion, the integration of public and expert opinions, etc. Given this, this paper proposes a public and large-scale expert information fusion method that considers public dynamic reliability via sentiment analysis and intuitionistic fuzzy number (IFN) expressions. First, sentiment analysis technology is used to process public social media data and obtain IFNs as the opinions of the public decision-making body. Second, the concept of public dynamic reliability is defined to measure the degree of integration of public opinion. Third, a novel information entropy measure of IFNs is proposed, and a new method is introduced to determine the criteria weights under the two different decision-making bodies. Finally, an optimization model that considers the consensus levels of expert subgroups is proposed to determine the weights of different decision-making bodies. The public and expert opinions are then aggregated to obtain collective decision-making information. A case study is proposed to illustrate the application of the proposed method, and the comparative analysis reveals the features and advantages of this model.     

The Double‐Edged Sword: The Effects of Journalists' Social Media Activities on Audience Perceptions of Journalists and Their News Products

As social media become popular news platforms, journalists and news organizations have been keen to capitalize on their potential to build and maintain audiences. However, little is known about the extent to which these efforts may have adverse implications. Based on normative theories, the present study investigates the influence of journalists' social media activities (specifically, self‐disclosure and interaction with other users) on audience perceptions of journalists. An experiment (N = 267) revealed that: Although both journalists' self‐disclosure and interaction positively influenced audience perceptions of the journalists in the personal dimension, interaction negatively influenced audience perceptions in the professional dimension; and the perceptions transferred to perceptions of news products, thereby mediating the relationship between journalists' social media activities and audience news perceptions.     

基于非理性博弈的舆情传播仿真建模研究

随着社交网络的不断发展,借助社交网络进行传播的舆情信息的威胁越来越大. 本文利用博弈论的方法研究了舆情信息的传播机制,从理性博弈和非理性博弈的角度出发对个体与邻居的交互行为进行建模. 随后以目前较为成熟的传染病模型为基础,通过引入传播学中的社会威慑因素来构建基于非理性博弈的舆情传播模型,以实现适应舆情传播研究的仿真系统,继而将基于非理性博弈的舆情传播模型在无标度网络和小世界网络上的动力学特征进行理论分析. 在仿真环境中对非理性博弈的舆情传播模型进行实验,分析模型参数、网络密度和舆情传播源对舆情传播的影响,其结果符合理论分析结果. 最后对无标度网络上舆情传播的控制策略进行了研究,包括:社会威慑方法、目标免疫方法和正面消息方法,为社交网络上的政策制定提供了理论基础.     

基于转发效应的自媒体网络舆情扩散

随着无线网络技术的发展与手机网民人数的激增,以微信、微博等新媒体为平台的自媒体网络舆情逐渐取代传统媒体舆情,成为舆论场上的主导力量,并对社会的稳定产生了重要影响.基于此,首先界定自媒体网络舆情转发网络的相关概念,论述转发网络的转发效应;然后剖析自媒体网络舆情转发效应的影响因素,构建基于转发效应的自媒体网络舆情扩散模型,并在此基础上,对舆情扩散模型进行了仿真分析.通过仿真分析发现,转发网络中的超级群组是自媒体网络舆情迅速扩散的中心和节点,使得自媒体网络舆情扩散出现了明显的动态舆情级联特征,进而使其在出现、增长与稳定等阶段具有发散性特征,并以合并、超级群组和集中式拓扑结构等效应形式进行着扩散和传播.     

基于Moran过程的竞争性舆情信息的网络传播博弈模型

新的媒体环境下,在享受不同形式在线社交网络提供便捷信息互动渠道的同时,需要加强对信息传播的管理与引导,防止其发展成为负面舆情信息肆意滋生的温床。结合传统演化博弈论与复杂网络理论,提出了竞争性舆情信息的网络传播博弈模型,并理论分析了正面舆情信息传播占优的收益条件及群体规模;随后,结合实际案例,通过仿真实验对比分析三种博弈环境下网络舆情的演化特征。三种博弈环境下,正面舆情信息传播策略占优条件均与收益有关,弱选择情境对收益参数的要求最为严格;强、弱选择情景下策略占优的条件还与群体规模有关,且强选择情景对群体规模的要求更为严格。     

面向应急管理的事故和灾害网络舆情监管系统

系统基于Hadoop框架,利用MapReduce编程模型,采用K-means聚类算法,结合实际舆情处置工作流程,将舆情监测、处理、分析、上传下达等功能融为一体.面向应急管理服务,针对主要灾害及政务类网络信息开展全天候监测,第一时间获取事件与灾害"原生态"舆情数据,并实现热点话题的快速识别与跟踪处理.通过省-市-区三级用...     

基于价值累加理论的网络热点事件舆情研判及系统实现

基于价值累加理论分析网络热点事件的演变过程,确定触发因素的出现、共同信念的形成、行动动员的完成是事件演变的三个关键环节,设计监测模型,对事件舆情中的敏感因素、情感的形成及扩散进行分析判断。在此基础上,构造网络舆情热点信息智能监测平台系统(NPOIMS),以我国西部地区的x市为实例对象,架构舆情监测系统,监测与x市有关的各类舆情信息,提炼热点词语,进行舆情研判,提供分析报告,为相关部门提供舆情引导和事件应对的信息参考和决策支持。     

自动词语简化方法综述

自动词语简化是用简单、同等意义的词语替代句子中复杂词的过程,是文本简化中的一个重要研究方向。随着自然语言处理技术的快速发展,词语简化方法也在不断更新与变化。该文对词语简化方法的相关研究进行了梳理,先对词语简化的整体框架进行解释,然后将词语简化方法总结为语言数据库、自动规则、词嵌入模型、混合模型和预处理语言模型。接着对汉语词语简化研究所面临的难点进行论述。最后,对词语简化方法的发展方向进行了展望和总结。