Analysis and Management of Flu Disease Public Opinion Based on Machine Learning

In the new media era, there are more ways of information dissemination, and the speed of information dissemination becomes faster. Along with it, various public opinions and rumors flood the cyberspace. As a mainstream social media information publishing platform, microblog has become the main way for netizens to obtain, disseminate and publish information. Because microblog can freely make speeches, and has a fast transmission speed and a wide range, it is easy for public opinion information to be widely disseminated in a short time. In particular, information such as rumors in public opinion can affect the network environment and social stability. Therefore, it is necessary to analyze and predict public opinion changes and to provide early warning. The literature uses the classic BP-NN (BP-NN) as the base prediction model, and uses the information published on the Sina microblog platform as a sample to analyze and predict the public opinion of influenza diseases. Due to the BP-NN’ slow convergence speed, this paper introduces an improved genetic algorithm to select the optimal parameters in the BP-NN (IGA-BP-NN), shorten the calculation time, and improve the analysis and prediction efficiency. The experiments verify that the work in this paper can provide more accurate early-warning information for the public opinion management of related departments.

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Deep Structured Learning for Natural Language Processing

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3433538 ◽

2021 ◽

Vol 20 (3) ◽

pp. 1-14

Author(s):

Yong Li ◽

Xiaojun Yang ◽

Min Zuo ◽

Qingyu Jin ◽

Haisheng Li ◽

...

Keyword(s):

Public Opinion ◽

Food Safety ◽

Language Processing ◽

Early Warning ◽

Conditional Random Field ◽

Semantic Features ◽

Related Sequence ◽

The Public ◽

Network Public Opinion

The real-time and dissemination characteristics of network information make net-mediated public opinion become more and more important food safety early warning resources, but the data of petabyte (PB) scale growth also bring great difficulties to the research and judgment of network public opinion, especially how to extract the event role of network public opinion from these data and analyze the sentiment tendency of public opinion comment. First, this article takes the public opinion of food safety network as the research point, and a BLSTM-CRF model for automatically marking the role of event is proposed by combining BLSTM and conditional random field organically. Second, the Attention mechanism based on vocabulary in the field of food safety is introduced, the distance-related sequence semantic features are extracted by BLSTM, and the emotional classification of sequence semantic features is realized by using CNN. A kind of Att-BLSTM-CNN model for the analysis of public opinion and emotional tendency in the field of food safety is proposed. Finally, based on the time series, this article combines the role extraction of food safety events and the analysis of emotional tendency and constructs a net-mediated public opinion early warning model in the field of food safety according to the heat of the event and the emotional intensity of the public to food safety public opinion events.

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Public Opinion and Foreign Policy

Oxford Research Encyclopedia of Politics ◽

10.1093/acrefore/9780190228637.013.472 ◽

2017 ◽

Cited By ~ 1

Author(s):

Douglas Foyle

Keyword(s):

Foreign Policy ◽

Public Opinion ◽

New Media ◽

Information Acquisition ◽

Opinion Dynamics ◽

Domestic Policy ◽

The Public ◽

The Media ◽

New Research ◽

Traditional Approaches

Dramatic changes in the way the public acquires information and formulates its attitudes have potentially altered the opinion and foreign policy relationship. While traditional approaches have treated public opinion on domestic and foreign matters as largely distinct, the culmination of a series of changes may eliminate the effective distinction between foreign and domestic policy, at least in terms of how the American political system operates. All the factors central to the opinion and foreign policy process, such as information acquisition, attitude formation, media effects, the effect of opinion on policy, and presidential leadership now appear to mirror the processes observed at the domestic level. This analysis reviews historical trends in the literature on public opinion and foreign policy that has focused on the rationality of the public’s opinions, the structure of its attitudes, and its influence on foreign policymaking. The traditional Almond-Lippmann consensus portrayed an emotional public with unstructured attitudes and little influence on foreign policy; however, revisionist views have described a reasonable public with largely structured views on foreign policy that can, at times, constrain and even drive those policies. More recently, the rise of “intermestic” issues, contain both domestic and international elements, such as globalization, inequality, terrorism, immigration, and climate change, have interacted to transform the domestic and international context. The bulk of this analysis highlights emerging new research directions that should be pursued in light of the changes. First, scholars should continue to evaluate the “who thinks what and why” questions with particular attention to differences between high- and low-information individuals, the effect of misinformation, and information sources. In doing so, research should build on research from non-American contexts that points to the important influences of societal and institutional factors. In addition to continued examination of traditional demographic factors such as partisanship and ideology, additional attention should turn to consider potential genetic and biological foundations of attitudes. Finally, researchers should continue to evaluate how the new media environment, including social media, affects how the public accesses information, how the media provides information, and how political elites attempt to shape both. Given these changes, scholars should consider whether it continues to make sense to treat public opinion dynamics regarding foreign policy as distinct from domestic policy and its implications.

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Multidimensional influencing factors of public opinion information dissemination in social media: Evidence from Weibo dataset

International Journal of Modern Physics B ◽

10.1142/s0217979219503752 ◽

2019 ◽

Vol 33 (31) ◽

pp. 1950375 ◽

Cited By ~ 2

Author(s):

Guanghui Wang ◽

Yufei Wang ◽

Kaidi Liu ◽

Jimei Li

Keyword(s):

Social Media ◽

Public Opinion ◽

Information Content ◽

Influencing Factors ◽

Network Structure ◽

Information Dissemination ◽

Influential Factors ◽

Channel Network ◽

The Public ◽

Information Characteristics

The factors influencing the dissemination of public opinion on social media, the main carrier of public opinion, are diverse, complex and changeable. Existing studies of influential factors of public opinion dissemination focus on the information itself and information sources in the dissemination process, failing to consider the comprehensive influence of multidimensional factors, such as information content, sources and channels. This study takes the identification of multidimensional influential factors of social media information dissemination as the research object and comprehensively sorts out the influencing factors of public opinion. To improve the scientific basis and accuracy of the research, multidimensional factors, including information characteristics, dissemination network structure and user-level attributes, are selected to analyze the effect of influential factors in different dimensions on the dissemination of social media public opinion information using econometric models. Three main conclusions of this paper are as follows: (1) The traditional information characteristics (information content) and information source attributes (user-level factor) are not the only key factors affecting information dissemination, while the information channel (network structure) is worth more consideration. (2) Netizens tend to pay more attention to the psychological and emotional attributes of information when forwarding public opinions. The communication mode in which offline social elites enlighten the public no longer exists; whether a user is a network celebrity or lives in the central area no longer significantly affects public opinion dissemination. (3) The higher the total amount of information users release, the more the information would interfere with the public opinion. This is mainly because users with a higher level of activity may release more invalid information about advertising that has nothing to do with public opinion events.

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Media Agenda-Setting and Gatekeeping: The Twitter Takeover of Traditional Mass Media Practices Through the Use of Networked Journalism

10.32920/ryerson.14664321.v1 ◽

2021 ◽

Author(s):

Vandon Gene

Keyword(s):

Mass Media ◽

New Media ◽

Agenda Setting ◽

Information Dissemination ◽

First Century ◽

Sources Of Information ◽

The Public ◽

Media Institutions ◽

Information Providers ◽

Media Practices

With a growing number of people moving away from traditional sources of information providers, towards new online sources, it has become evident that the agenda setting and gatekeeping functions of the past have been altered. Due to such alteration, it can be said that the profession of information dissemination has all but evaporated into a cesspool of opinion that has been framed to uphold the viewpoints of a particular ideology. While most studies to date have been effective in highlighting the alteration of agenda-setting and gatekeeping, this paper attempts to focus on the shift in such practices, away from traditional mass media institutions, to a new form of media through the practices of networked journalism. In order to demonstrate the following, this paper uses the 2016 U.S. Presidential Election as a case study. Tweets from traditional mass media institutions, new media institutions (such as thought opinion leaders), and the public are collected and examined in relation to information dissemination, via topic coverage. An analysis of these tweets confirms such shift in agenda-setting and gatekeeping, where the powers of information dissemination move away from traditional mass media institutions, towards a model of information that is dependent upon the public and its engagement of such information. This study is part of a larger body of research on the twenty-first century phenomenon of publicly sourced information dissemination in the networked society. In focusing on the shift that is occurring within society, this study will contribute to future publications on a similar topic

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Media Agenda-Setting and Gatekeeping: The Twitter Takeover of Traditional Mass Media Practices Through the Use of Networked Journalism

10.32920/ryerson.14664321 ◽

2021 ◽

Author(s):

Vandon Gene

Keyword(s):

Mass Media ◽

New Media ◽

Agenda Setting ◽

Information Dissemination ◽

First Century ◽

Sources Of Information ◽

The Public ◽

Media Institutions ◽

Information Providers ◽

Media Practices

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COVID-19 Information Dissemination Using the WeChat Communication Index: Retrospective Analysis Study (Preprint)

10.2196/preprints.28563 ◽

2021 ◽

Author(s):

Zina Fan ◽

Wenqiang Yin ◽

Han Zhang ◽

Dandan Wang ◽

Chengxin Fan ◽

...

Keyword(s):

Public Opinion ◽

Information Dissemination ◽

Descriptive Analysis ◽

Three Dimensions ◽

The Public ◽

Related Information ◽

Information Governance ◽

People’S Daily ◽

People's Daily ◽

Types Of Information

BACKGROUND The COVID-19 outbreak has tremendously impacted the world. The number of confirmed cases has continued to increase, causing damage to society and the economy worldwide. The public pays close attention to information on the pandemic and learns about the disease through various media outlets. The dissemination of comprehensive and accurate COVID-19 information that the public needs helps to educate people so they can take preventive measures. OBJECTIVE This study aimed to examine the dissemination of COVID-19 information by analyzing the information released by the official WeChat account of the People’s Daily during the pandemic. The most-read COVID-19 information in China was summarized, and the factors that influence information dissemination were studied to understand the characteristics that affect its dissemination. Moreover, this was conducted in order to identify how to effectively disseminate COVID-19 information and to provide suggestions on how to manage public opinion and information governance during a pandemic. METHODS This was a retrospective study based on a WeChat official account. We collected all COVID-19–related information, starting with the first report about COVID-19 from the People’s Daily and ending with the last piece of information about lifting the first-level emergency response in 34 Chinese provinces. A descriptive analysis was then conducted on this information, as well as on Qingbo Big Data’s dissemination index. Multiple linear regression was utilized to study the factors that affected information dissemination based on various characteristics and the dissemination index. RESULTS From January 19 to May 2, 2020, the People’s Daily released 1984 pieces of information; 1621 were related to COVID-19, which mainly included headline news items, items with emotional content, and issues related to the pandemic’s development. By analyzing the dissemination index, seven information dissemination peaks were discerned. Among the three dimensions of COVID-19 information—media salience, content, and format—eight factors affected the spread of COVID-19 information. CONCLUSIONS Different types of pandemic-related information have varying dissemination power. To effectively disseminate information and prevent the spread of COVID-19, we should identify the factors that affect this dissemination. We should then disseminate the types of information the public is most concerned about, use information to educate people to improve their health literacy, and improve public opinion and information governance.

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Strategic Optimization of Public Opinion Management in Universities under Change of Network Public Opinion Ecosystem

Journal of Education and Culture Studies ◽

10.22158/jecs.v4n4p28 ◽

2020 ◽

Vol 4 (4) ◽

pp. p28

Author(s):

Shui Jingjing

Keyword(s):

Public Opinion ◽

New Media ◽

Ecological Change ◽

Management Mechanism ◽

The Public ◽

Network Media ◽

Education Of Students ◽

New Situation ◽

Class Construction ◽

Network Public Opinion

The current breakthroughs in Internet big data and AI technologies have accelerated the fission-like dissemination of public opinion on the Internet, providing both opportunities and challenges for university governance. Universities should adapt to the new situation of the ecological change of public opinion with subject, object, carrier and environment as the elements, and optimize the public opinion management mechanism of universities from five levels: building a management system of network public opinion, strengthening the guidance mode of public opinion, promoting the operation of campus new media matrix, paying attention to the education of students’ network media literacy, and focusing on the construction of open internal and external communication platform, purifying the network space, maintaining the image of universities, and creating a Double First-class construction of universities and necessary ecology.

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Analysis of Public Opinion in Colleges and Universities Based on Wireless Web Crawler Technology in the Context of Artificial Intelligence

Mobile Information Systems ◽

10.1155/2022/7745028 ◽

2022 ◽

Vol 2022 ◽

pp. 1-10

Author(s):

WenNing Wu ◽

ZhengHong Deng

Keyword(s):

Artificial Intelligence ◽

Public Opinion ◽

Colleges And Universities ◽

Web Crawler ◽

The Public ◽

User Influence ◽

Proposed Model ◽

Wide Range ◽

Different Dimensions ◽

The Web

Wi-Fi-enabled information terminals have become enormously faster and more powerful because of this technology’s rapid advancement. As a result of this, the field of artificial intelligence (AI) was born. Artificial intelligence (AI) has been used in a wide range of societal contexts. It has had a significant impact on the realm of education. Using big data to support multistage views of every subject of opinion helps to recognize the unique characteristics of each aspect and improves social network governance’s suitability. As public opinion in colleges and universities becomes an increasingly important vehicle for expressing public opinion, this paper aims to explore the concepts of public opinion based on the web crawler and CNN (Convolutional Neural Network) model. Web crawler methodology is utilised to gather the data given by students of college and universities and mention them in different dimensions. This CNN has robust data analysis capability; this proposed model uses the CNN to analyse the public opinion. Preprocessing of data is done using the oversampling method to maximize the effect of classification. Through the association of descriptions, comprehensive utilization of image information like user influence, stances of comments, topics, time of comments, etc., to suggest guidance phenomenon for various schemes, helps to enhance the effectiveness and targeted social governance of networks. The overall experimentation was carried out in python here in which the suggested methodology was predicting the positive and negative opinion of the students over the web crawler technology with a low rate of error when compared to other existing methodology.

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COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter

Vaccines ◽

10.3390/vaccines9101059 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1059

Author(s):

Amir Karami ◽

Michael Zhu ◽

Bailey Goldschmidt ◽

Hannah R. Boyajieff ◽

Mahdi M. Najafabadi

Keyword(s):

Public Opinion ◽

Statistical Tests ◽

Small Scale ◽

Success Factor ◽

Great Opportunity ◽

Public Response ◽

The Public ◽

Wide Range ◽

Significant Difference ◽

Negative Sentiment

The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. To understand the public response, there is a need to explore public opinion. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. Twitter can provide a great opportunity to understand public opinion regarding COVID-19 vaccines. The current study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets to provide a wider perspective on the COVID-19 vaccine. This study identifies the sentiment of tweets using a machine learning rule-based approach, discovers major topics, explores temporal trend and compares topics of negative and non-negative tweets using statistical tests, and discloses top topics of tweets having negative and non-negative sentiment. Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. We found Twitter users have discussed a wide range of topics from vaccination sites to the 2020 U.S. election between November 2020 and February 2021. The findings show that there was a significant difference between tweets having negative and non-negative sentiment regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses. This research illustrates that Twitter data can be used to explore public opinion regarding the COVID-19 vaccine.

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The Politics of Public Broadcasters in Africa

Oxford Research Encyclopedia of Politics ◽

10.1093/acrefore/9780190228637.013.816 ◽

2019 ◽

Author(s):

Corinna Arndt

Keyword(s):

New Media ◽

Freedom Of Expression ◽

Important Variable ◽

Urban Centers ◽

The Public ◽

Commercial Media ◽

Political Determinants ◽

Wide Range ◽

The Government ◽

Set Up

National broadcasters are a standard feature across Africa. Set up by colonial regimes, they dominate media landscapes with their unrivaled geographic reach. Radio continues to be the main—and often only—source of information outside urban centers, where commercial media struggle to survive and illiteracy remains a challenge. Although access to new media has risen exponentially, use of mobile technology continues to be prohibitively expensive. Some national broadcasters are official state broadcasters: owned, run, and editorially controlled by government. However, many claim to be public broadcasters. By definition, these are accountable to the public rather than the government of the day: accessible to a universal audience, inclusive of a wide range of views; and fair, balanced, and independent in their journalism. This aspiration is reflected in national and supranational policy such as the African Charter on Broadcasting and the Declaration of Principles on Freedom of Expression in Africa. In reality, these broadcasters lack de jure independence, the basic condition for them to be considered “public.” They are, in law and in practice, state broadcasters—owed to a range of historical, social, financial, and political determinants despite attempts by journalists and civil society to change this. Principally, the political will has been lacking—in colonial as well as postcolonial elites—to relinquish control of newsrooms and open up space for dissent. There is one exception: the South African Broadcasting Corporation was granted de jure independence following apartheid and enjoys unrivaled (though contested) legal guarantees and journalistic freedom. Its ongoing difficulties to fully meet its public broadcasting mandate despite this relatively conducive environment demonstrate that de jure independence is a necessary but not sufficient condition for successful broadcasting transformation, and that organizational culture is an important variable to be taken into account.

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