Research on crisis management of enterprise network public opinion

With the arrival ofthe era from the media, the internet spread over all sectors ofsocicty, affect people＇s political, economic and cultural life. The network has become the main place, people express their views so also. And the popularity of media enables consumers to easily express their products, services and other aspects of the topic, spread on the network at breakneck speed, and other users of the attention and support, thus forming a kind of public opinion, influence The development of enterprises, which will undoubtedly bring unprecedented pressure, constant attention, coupled with people＇s comments, forwarding, dissemination media, making the crisis spread, enterprises should not reasonable, effective, will enable enterprises to establish a sense of trust collapsed, brought a negative impact to the enterprise.

Innovation of Ideological and Political Education in Colleges and Universities from the Perspective of Network Public Opinion

In today's era of rapid development of network media,network public opinion has brought great changes to people's lives.As a place of frequent network public opinion,colleges and universities create a more complex and changeable network environment.This is both an opportunity and a challenge to the ideological and political education in universities.Therefore,through a brief introduction of the connotation and characteristics of network public opinion in colleges and universities,this article explores the innovation of ideological and political education in the context of network public opinion in terms of educational concepts,contents,and methods to ensure that this education plays a positive role in the new era.

Hot Events Detection of Stock Market Based on Time Series Data of Stock and Text Data of Network Public Opinion

With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and enables investors to quickly identify relevant financial events that may lead to stock market volatility. However, in the research of event detection in the financial field, many studies are focused on micro-blog, news and other network text information. Few scholars have studied the characteristics of financial time series data. Considering that in the financial field, the occurrence of an event often affects both the online public opinion space and the real transaction space, so this paper proposes a multi-source heterogeneous information detection method based on stock transaction time series data and online public opinion text data to detect hot events in the stock market. This method uses outlier detection algorithm to extract the time of hot events in stock market based on multi-member fusion. And according to the weight calculation formula of the feature item proposed in this paper, this method calculates the keyword weight of network public opinion information to obtain the core content of hot events in the stock market. Finally, accurate detection of stock market hot events is achieved.

The Transmission Mechanism and Control Strategy of the Network Public Opinion in Frontier Ethnic Regions

Social networking has become an important channel for netizens to express public Opinions and reflect social conditions and public opinions. At the same time, social networks also provide favorable conditions for the widespread dissemination of negative information. The spread of negative information will weaken the credibility of the government and affect government decisions. For this reason, the network public opinion transmission mechanism and control strategy in frontier ethnic areas are studied in depth. Through the analysis of the stage of social media public opinion dissemination, a more reasonable social media public opinion dissemination stage model was concluded, and the social media public opinion dissemination mechanism and the linkage mechanism of the dissemination mechanism were derived, which solved the problem of how social media sentiment was disseminated. According to the analysis of the stage characteristics of social media public opinion dissemination , the six stages of public opinion transmission were divided into different levels , and corresponding strategies were proposed for different levels of the dissemination stage.

Predicting Stock Closing Price with Stock Network Public Opinion Based on AdaBoost-AAFSA-Elman Model and CEEMDAN Algorithm

To solve low prediction accuracy of Elman in predicting stock closing price,the model of adaptive boosting(AdaBoost)-improved artificial fish swarm algorithm(AAFSA)-Elman based on complete ensemble em-pirical mode decomposition with adaptive noise(CEEMDAN)is proposed.By adding different white noise to the original data,CEEMDAN algorithm is used to decompose attributes serial selected by Boruta algorithm and text mining.To optimize the weight and threshold of Elman,self-adaption step length and view scope are used to improve artificial fish swarm algorithm(AFSA).AdaBoost algorithm is used to compose 5 weak AAFSA-Elman predictors into a strong predictor by continuous iteration.Experiments show that the mean absolute percentage error(MAPE)of AdaBoost-AAFSA-Elman model reduces from 4.9423%to 1.2338%.This study provides an experimental method for the prediction of stock closing price based on network public opinio.

The Research on Social Networks Public Opinion Propagation Influence Models and Its Controllability

Public opinion propagation control is one of the hot topics in contemporary social network research. With the rapid dissemination of information over the Internet, the traditional isolation and vaccination strategies can no longer achieve satisfactory results. A positive guidance technology for public opinion diffusion is urgently needed. First, based on the analysis of influence network controllability and public opinion diffusion, a positive guidance technology is proposed and a new model that supports external control is established. Second, in combination with the influence network, a public opinion propagation influence network model is designed and a public opinion control point selection algorithm(POCDNSA) is proposed. Finally, An experiment verified that this algorithm can lead to users receiving the correct guidance quickly and accurately, reducing the impact of false public opinion information; the effect of CELF is no better than that of the POCDNSA algorithm. The main reason is that the former is completely based on the diffusion cascade information contained in the training data, but does not consider the specific situation of the network structure and the diffusion of public opinion information in the closed set. thus, the effectiveness and feasibility of the algorithm is proven. The findings of this article therefore provide useful insights for the implementation of public opinion control.

Research on Prediction of Sentiment Trend of Food Safety Public Opinion

Emotion has a nearly decisive role in behavior, which will directly affect netizens’ views on food safety public opinion events, thereby affecting the development direction of public opinion on the event, and it is of great significance for food safety network public opinion to predict emotional trends to do a good job in food safety network public opinion guidance. In this paper, the dynamic text representation method XLNet is used to generate word vectors with context-dependent dependencies to distribute the text information of food safety network public opinion. Then, the word vector is input into the CNN-BiLSTM network for local semantic feature and context semantic extraction. The attention mechanism is introduced to give different weights according to the importance of features, and the emotional tendency analysis is carried out. Based on sentiment analysis, sentiment value time series data is obtained, and a time series model is constructed to predict sentiment trends. The sentiment analysis model proposed in this paper can well classify the sentiment of food safety network public opinion, and the time series model has a good effect on the prediction of food safety network public opinion sentiment trend. .

Prediction of network public opinion based on bald eagle algorithm optimized radial basis function neural network

Purpose-In the new era of highly developed Internet information,the prediction of the development trend of network public opinion has a very important reference significance for monitoring and control of public opinion by relevant government departments.Design/methodology/approach-Aiming at the complex and nonlinear characteristics of the network public opinion,considering the accuracy and stability of the applicable model,a network public opinion prediction model based on the bald eagle algorithm optimized radial basis function neural network(BES-RBF)is proposed.Empirical research is conducted with Baidu indexes such as“COVID-19”,“Winter Olympic Games”,“The 100th Anniversary of the Founding of the Party”and“Aerospace”as samples of network public opinion.Findings-The experimental results show that the model proposed in this paper can better describe the development trend of different network public opinion information,has good stability in predictive performance and can provide a good decision-making reference for government public opinion control departments.Originality/value-A method for optimizing the central value,weight,width and other parameters of the radial basis function neural network with the bald eagle algorithm is given,and it is applied to network public opinion trend prediction.The example verifies that the prediction algorithm has higher accuracy and better stability.

The Study of SINA micro-blog Opinion Leaders Effect on Network Public Opinion Transmission

Opinion leaders play a critical role in network public opinion transmission, their perspectives can shape public opinion and influence policy formulation and implementation. The paper is based on SINA micro-blog, by structural equation model, as Fudan Poisoning Event for example, On the basis of in-depth analysis of opinion leaders effect on network public opinion transmission characteristics, Explore the opinion leaders on the influence of network public opinion transmission mechanism, in order to better play a role of opinion leader＇s guidance of public opinion.

Research on Public Opinion Transmission and Evolution Mechanism in Network

As the Internet continues to expand, the influence of network information on real life has gradually deepened. Research on propagation and evolution of Internet public opinion has become a hot topic. The force of intemet public opinion penetrates and influences on every aspect of the society. Compared with traditional public opinion, the network public opinion has features of immediate, multivariate and interactive, the propagation behavior has the new change compared with the traditional public opinion. In the propagation behavior of network public opinion, agenda setting is no longer an arbitrary, the influence of opinion leaders in the agenda setting becomes more and more complex and diversified. The formation time of network public opinion is short, and social influence becomes strong. Guide the public opinion to build a harmonious environment of network public opinion. Overall, our country＇ s network public opinion environment is a benign situation and steadily promoting the reforms of public policies. Although there is still a few not rational voices full of them, the network public opinion shows a general trend of positive thinking. Based on this philosophy, through the research of network public opinion dissemination and evolution mechanism, can be all-round good guidance and supervision of public opinion, building a harmonious network environment of public opinion.

Two-Layer Coupled Network Model for Topic Derivation in Public Opinion Propagation

In view of the fact that news can generate derivative topics when it spreads through micro-blogs,a two-layer coupled SEIR public opinion propagation model is proposed in this paper.The model divides the process of public opinion propagation into two layers:the original topic layer and the derived topic layer.Messages are transmitted separately by the SEIR model in the two topic layers,which are independent and interactive.The influence of the topic derivation rate on the propagation trend is established by solving for the equilibrium point and propagation threshold.Further,we establish the relationship between the original topic and the derived topic by simulation.This paper uses the Baidu index to demonstrate the correctness of the model.The relationship between the derived topic and the original topic is verified by adjusting the parameters by the control variable method.The results show that the proposed model is consistent with the propagation of actual public opinion.

Simulating the Spreading of Two Competing Public Opinion Information on Complex Network

In this paper, we investigate the regularity of spreading of information and public opinions towards two competing products in complex networks. By building its mathematical model and simulating its evolution process, we have found the statistical regularity for support rates of two different products at a steady stage. The research shows that strength of the public opinion spreading is proportional to the final support rates of a product.

Public Sentiment Analysis of Social Security Emergencies Based on Feature Fusion Model of BERT and TextLevelGCN

At present, the emotion classification method of Weibo public opinions based on graph neural network cannot solve the polysemy problem well, and the scale of global graph with fixed weight is too large. This paper proposes a feature fusion network model Bert-TextLevelGCN based on BERT pre-training and improved TextGCN. On the one hand, Bert is introduced to obtain the initial vector input of graph neural network containing rich semantic features. On the other hand, the global graph connection window of traditional TextGCN is reduced to the text level, and the message propagation mechanism of global sharing is applied. Finally, the output vector of BERT and TextLevelGCN is fused by interpolation update method, and a more robust mapping of positive and negative sentiment classification of public opinion text of “Tangshan Barbecue Restaurant beating people” is obtained. In the context of the national anti-gang campaign, it is of great significance to accurately and efficiently analyze the emotional characteristics of public opinion in sudden social violence events with bad social impact, which is of great significance to improve the government’s public opinion warning and response ability to public opinion in sudden social security events. .

Online Public Opinion Guidance Strategy for College Students in the Era of We Media

In the era of We Media, online public opinion in colleges and universities has become an important front of ideological and political, with some characteristics such as concealment, diversity and sensitivity, interaction and immediacy of communication. It is urgent to carry out some researches about network public opinion analysis and guidance mechanism. It is a new challenge that the ideological and political education in colleges and universities, must face how to guide online public opinion effectively. The team building should be strengthened and the right of public opinion should be returned. The platform construction should be enriched, and it is important to give the chance to various media. Therefore, it should strengthen the mechanism construction, and public opinion guidance must be scientific and professional. Colleges and universities should build a clear network space from such aspects as the construction of network public opinion guidance institutions, the cultivation of opinion leaders, the perfection of early warning mechanism, the improvement of university network literacy and the guidance of campus media.

网络舆情用户传播行为的心理距离感知强度测度及实证研究

[目的/意义]网络舆情用户传播行为的形成与网民的心理活动密切相关。从心理学视角出发,对网民传播群体的心理距离进行解析量化,有助于从心理根源上厘清网民传播行为的形成机理,为政府各级网信办提供理论和实践方法参考。[方法/过程]基于心理学理论视角,对心理距离进行归纳演绎,析出时空涨落度、语义关联度和情感认同度三个子参数,用以测度心理距离感知强度,并对感知强度进行分型。[结果/结论]为网络舆情研究提供了新的理论视角,心理距离感知强度能够很好地展现出舆情发展的时空热度、语义主题演化和舆情情感反转态势,能为舆情管控工作提供科学的理论支撑和量化依据。

基于情感分析的网络舆情共振研究

为有效应对复杂多变的舆情环境,研究孤立发生的单一舆情事件演化成具有某些相同特征的多舆情事件簇或事件集,针对同议题、同情绪等多起舆情事件建立网络舆情共振模型,通过爬取“唐山打人案”和“唐山打人事件被害人首次发声”事件相关微博数据,以及“2021年河南遭遇特大暴雨”和“2023年河北暴雨”相关微博数据,将评论数据进行BosonNLP情感分析,得出其情感分数;并将情感分数作为模型参数,分别对2起不同类型的案例进行检验。研究结果表明:在原生舆情与次生舆情共同作用下,引起网民情绪感染和矛盾冲突,从而发生网络舆情共振,并且共振产生的热度高于单一事件热度;网民消极的态度值会加速舆情共振、不同类型的事件所产生的舆情共振效果是不同的。研究结果可丰富网络舆情以及社会物理学相关理论,可为构建舆情共振的研究框架提供参考。

投资者网络舆论关注对MD&A语调操纵的影响研究

以2010~2021年中国A股上市公司为研究样本,探究网络舆论关注对企业管理层讨论与分析语调操纵行为的影响。研究发现,投资者网络舆论关注和管理层讨论与分析语调操纵行为显著正相关,二者之间的关系符合市场压力假说,而非有效监督假说。进一步发现,当企业盈余管理操纵空间越小、企业业绩压力越大和越需要进行形象管理时,管理层通过语调操纵迎合投资者网络舆论关注的行为越严重;语调操纵行为会加剧企业股票崩盘风险,降低企业价值;企业内部控制质量和机构投资者持股能缓解管理层通过语调操纵迎合投资者关注的行为。

基于改进SEIQR模型的突发网络舆情传播与控制研究

[目的/意义]社会突发事件映射到互联网空间中形成的突发网络舆情在不良因素的干扰下于网民间扩散容易形成强烈的负面情绪导向,很可能对网络社会的和谐稳定产生极大破坏。因此,正确认识突发网络舆情的传播特点,把握各种舆情管控措施对传播的影响,对合理、有效地管理舆情至关重要。[方法/过程]针对突发网络舆情的传播特性对标准SEIQR模型进行改进,提出实施舆情管控措施下以媒体恶意炒作和网民间恶性正反馈为主要传播驱动的SEACBR模型,并利用仿真实验对不同规模的突发网络舆情在各种管控措施下的传播过程进行模拟,以探究不同措施的管控效果并验证模型的泛化性。[结果/结论]管控突发网络舆情应以良性引导为主,其他措施为辅,这样在及时施策、积极治理的情况下突发网络舆情的消极影响就可被有效抑制。