Network Agenda Setting Model Research Articles

It’s not an encyclopedia, it’s a market of agendas: Decentralized agenda networks between Wikipedia and global news media from 2015 to 2020

Many innovative forms of media entered the online mediascape and can potentially set public agendas. This study drew on peer-produced news content on Wikipedia and theorized its unprecedented agenda building power within a network of diverse media sources. Adopting the network agenda-setting model, this study collected comprehensive global news coverage and Wikipedia coverage of top US political news events from 2015 to 2020. Time series analysis found that none of the media types (Wikipedia, elite media, and non-elite media) exhibited dominant agenda-setting power, while each of them can lead the agenda in certain circumstances. Wikipedia was a critical agenda setter for other media entities, and it also reflected the public’s collective evaluation of existing news agendas from multiple sources. This article proposed a multi-agent and multidirectional network architecture to describe agenda-setting relationships. We also highlighted four unique characteristics of Wikipedia that matter for digital journalism.

The application of network agenda setting model during the COVID-19 pandemic based on latent dirichlet allocation topic modeling.

Based on Network Agenda Setting Model, this study collected 42,516 media reports from Party Media, commercial media, and We Media of China during the COVID-19 pandemic. We trained LDA models for topic clustering through unsupervised machine learning. Questionnaires (N = 470) and social network analysis methods were then applied to examine the correlation between media network agendas and public network agendas in terms of explicit and implicit topics. The study found that the media reports could be classified into 14 topics by the LDA topic modeling, and the three types of media presented homogeneity in the topics of their reports, yet had their own characteristics; there was a significant correlation between the media network agenda and the public network agenda, and the We Media reports had the most prominent effect on the public network agenda; the correlation between the media agenda and the implicit public agenda was higher than that of the explicit public agenda. Overall, findings showed a significant correlation between network agendas among different media.

The Mutual Influence of the World Health Organization (WHO) and Twitter Users During COVID-19: Network Agenda-Setting Analysis.

BackgroundLittle is known about the role of the World Health Organization (WHO) in communicating with the public on social media during a global health emergency. More specifically, there is no study about the relationship between the agendas of the WHO and Twitter users during the COVID-19 pandemic.ObjectiveThis study utilizes the network agenda-setting model to investigate the mutual relationship between the agenda of the WHO’s official Twitter account and the agenda of 7.5 million of its Twitter followers regarding COVID-19.MethodsContent analysis was applied to 7090 tweets posted by the WHO on Twitter from January 1, 2020, to July 31, 2020, to identify the topics of tweets. The quadratic assignment procedure (QAP) was used to investigate the relationship between the WHO agenda network and the agenda network of the 6 Twitter user categories, including “health care professionals,” “academics,” “politicians,” “print and electronic media,” “legal professionals,” and the “private sector.” Additionally, 98 Granger causality statistical tests were performed to determine which topic in the WHO agenda had an effect on the corresponding topic in each Twitter user category and vice versa.ResultsContent analysis revealed 7 topics that reflect the WHO agenda related to the COVID-19 pandemic, including “prevention,” “solidarity,” “charity,” “teamwork,” “ill-effect,” “surveillance,” and “credibility.” Results of the QAP showed significant and strong correlations between the WHO agenda network and the agenda network of each Twitter user category. These results provide evidence that WHO had an overall effect on different types of Twitter users on the identified topics. For instance, the Granger causality tests indicated that the WHO tweets influenced politicians and print and electronic media about “surveillance.” The WHO tweets also influenced academics and the private sector about “credibility” and print and electronic media about “ill-effect.” Additionally, Twitter users affected some topics in the WHO. For instance, WHO followers affected “charity” and “prevention” in the WHO.ConclusionsThis paper extends theorizing on agenda setting by providing empirical evidence that agenda-setting effects vary by topic and types of Twitter users. Although prior studies showed that network agenda setting is a “one-way” model, the novel findings of this research confirm a “2-way” or “multiway” effect of agenda setting on social media due to the interactions between the content creators and audiences. The WHO can determine which topics should be promoted on social media during different phases of a pandemic and collaborate with other public health gatekeepers to collectively make them salient in the public.

Media coverage and public perceptions of the THAAD event in China, the United States, and South Korea: a cross-national network agenda-setting study

Based on original data gathered from China, the United States, and South Korea, this study explores the relationship between media representations and public opinions concerning the deployment of the Terminal High Altitude Area Defense (THAAD) system in South Korea. Further, this study validates the network agenda-setting (NAS) model in multiple social contexts. This study’s supportive results confirm the NAS effects on both implicit and explicit public agendas, further expanding current NAS research. From a cross-nationally comparative perspective, this study investigates how NAS effects vary across countries, which is particularly important in the age of globalization, offering valuable contributions to an improved understanding of the NAS model. The study’s findings illustrate substantial differences between media agendas and variations in the NAS effects across countries, providing empirical evidence of national traits as potential contingent agenda-setting conditions.

Exploring inter-media agenda-setting effects: Network agenda-setting model by using big-data analysis

Exploring inter-media agenda-setting effects: Network agenda-setting model by using big-data analysis

Delineating the Transnational Network Agenda-Setting Model of Mainstream Newspapers and Twitter: A Machine-Learning Approach

ABSTRACT The months-long anti-extradition bill movement in Hong Kong has gained worldwide attention. Grounded in the network agenda-setting (NAS) model, this study utilizes a machine-learning approach to analyze the related coverage of mainstream newspapers in Hong Kong, Mainland China, the U.S. and the U.K. (N = 2118), as well as discussions on Twitter (N = 152,509). Network visualizations showed that each media utilized a unique approach to highlight and connect the substantive and affective attributes. Time-series analyses revealed an overall reciprocal whilst asymmetrical association between the newspapers and Twitter, in which the latter exhibited a stronger influence on the former, particularly in terms of substantive attribute agendas. Yet, Twitter’s impact shrank in terms of the affective attribute agendas and the NAS models. Newspapers, though exerted rather limited impact on Twitter, maintained a certain extent of independence in setting their affective attribute agendas and NAS models. This study enriches the NAS literature through combining substantive and affective attributes in semantic networks.

Political speeches and media agenda: electoral rigging movement — 2013 as a building factor of media agenda in Pakistan

Objectives: To explore the relationship of Pakistani Political Leader Imran Khan Speeches and newspapers agenda examining the coverage of electoral rigging issue which gained the momentum of a movement in Pakistan during post elections 2013 against the then siting government of Pakistan Muslim League Nawaz\'s (PML-N). Method: The study takes its roots from the network agenda setting model. It aims to study the model in the context of agenda building in Pakistan. Social Network Analysis technique was used to analyze the data collected from two sources including: Live speeches of movement leader, Imran Khan and coverage of three leading Urdu dailies of Pakistan; Express, Dunya and Jang to explore the relationship among these agenda setting networks. Firstly, we performed content analysis of 128 speeches of Imran Khan, 964 news stories and 239 editorials of sample newspapers. Secondly, we turned data into matrices and applied network analysis techniques with the help of UCINet and NetDraw software. Findings: QAP Correlation indicates strong association among, Imran Khan Speeches and newspapers agenda. However, these associations are different for two issues; Electoral rigging, and PML-N rigging. Imran Khan influenced the media agenda more for shaping the image of elections-2013 as unfair and rigged. On the other hand, his speeches were found less influential in shaping media agenda about the involvement of PML-N in electoral rigging. In this way, study reveals that third level agenda building effects depend upon the political nature of issues. Keywords: Third level agenda building; political speeches; electoral rigging; Pakistan elections-2013; social network analysis

One name can change many things

Abstract As an application study of the network agenda-setting model, this study examines how the media and public network agendas can differ, based on which political candidate was mentioned along with the immigration issue in news coverage and in public tweets. Through network analyses, this study shows that there were differences in the salient attributes of the immigration issue, and that the dominant narrative structure of the issue depended on which political candidate was mentioned.

Whose Story Wins on Twitter?

The South China Sea dispute is one of the most complicated geopolitical issues of the twenty-first century. While this international conflict revolves around military and economic disputes, in today’s information age international politics also hinges on how each country presents the dispute in the news and whose “story” wins. Based on the Network Agenda Setting model, this study analyzed the news coverage in three involved countries—China, the Philippines, and the United States—and examined whose “story” gains the most prominence on Twitter, an emerging transnational public sphere. A combination of network analysis and the Granger causality test were used to explicate the media effects. Network visualization techniques were adopted to graphically represent the media network agendas. Overall, the results showed that the Twittersphere to a great extent followed the US news media in reasoning the association and dynamics between different countries involved in the South China Sea dispute, whereas the media in China and the Philippines showed minimal to moderate impact on this global social media platform. The findings demonstrate that the US media’s agenda-setting effect can transcend national borders, and that the imbalanced power structure has been reinforced in this new media environment.

Beyond Salience Transmission: Linking Agenda Networks Between Media and Voters

This study investigated the network agenda setting (NAS) model with data gathered from Taiwan’s 2012 presidential election. Networks of important objects and candidate attributes in the news were compared with the counterparts generated from public opinion. The overall correlation between the media and public network agendas was positive and significant, thus supporting the NAS model in a non-Western context. In addition, this study found that the NAS model offered more predictive power at the attribute than the object level. The effects of selective exposure in a partisan media system were also incorporated into the investigation. Results showed that partisan selective exposure did not lead to consistent findings about the accentuated association between like-minded media consumption and candidate evaluation.

Networks, Big Data, and Intermedia Agenda Setting: An Analysis of Traditional, Partisan, and Emerging Online U.S. News

This large-scale intermedia agenda–setting analysis examines U.S. online media sources for 2015. The network agenda–setting model showed that media agendas were highly homogeneous and reciprocal. Online partisan media played a leading role in the entire media agenda. Two elite newspapers— The New York Times and The Washington Post—were found to no longer be in control of the news agenda and were more likely to follow online partisan media. This article provides evidence for a nuanced view of the network agenda–setting model; intermedia agenda–setting effects varied by media type, issue type, and time periods.

The Power of Message Networks: A Big-Data Analysis of the Network Agenda Setting Model and Issue Ownership

This article presents an empirical study that tests a new concept, “issue ownership network,” which is based on the network agenda setting (NAS) model and the theory of issue ownership. Big data analytics and semantic network analysis were used to examine the large data set collected on Twitter during the 2012 U.S. presidential election. Results showed that the news media could determine the public's identification of a political candidate with not just individual issues but also entire networks of issues. Here we argue that traditional news media still set the public agenda in this new media environment, and do so in ways more complicated through constructing message networks. The study also demonstrates that the NAS model and its unique focus can potentially enrich the understanding of other communication and social science theories and concepts.

Exploring “the World Outside and the Pictures in Our Heads”

This study examines the Network Agenda Setting Model, the third level of agenda-setting theory. It seeks to expand the model’s scope by testing five years (2007-2011) of aggregated data from national news media and polls. The study finds evidence that the news media bundled issue objects and made them salient in the public’s mind. Findings of the study also demonstrate strong network correlations of issue salience among different types of news media.

Network Issue Agendas on Twitter During the 2012 U.S. Presidential Election

This study finds support for agenda melding and further validates the Network Agenda Setting (NAS) model through a series of computer science methods with large datasets on Twitter. The results demonstrate that during the 2012 U.S. presidential election, distinctive audiences “melded” agendas of various media differently. “Vertical” media best predicted Obama supporters’ agendas on Twitter whereas Romney supporters were best explained by Republican “horizontal” media. Moreover, Obama and Romney supporters relied on their politically affiliated horizontal media more than their opposing party’s media. Evidence for findings are provided through the NAS model, which measures the agenda-setting effect not in terms of issue frequency alone, but also in terms of the interconnections and relationships issues inside of an agenda. doi:10.1111/jcom.12089 Big Data and computer science methods can further the findings of agenda-setting theory. Using Twitter, ap opular microblogging service, we show how supporters of Barack Obama and supporters of Mitt Romney reacted to different media agendas during the 2012 U.S. presidential election. We do so using a network analysis perspective, which shows how issues from the election were talked about in relationship to each other. The results give us a clear, large-scale picture of how the media influenced different audiences. We also offer updates to the emerging theory of agenda melding and the Network Agenda Setting (NAS) model. We have arrived at these conclusions by utilizing several computer science methods, including automated data mining and aggregation, sentiment analysis, network analysis, machine learning, and computer-assisted content analysis. Why Twitter data? In a world of social media, Twitter differentiates itself in two ways: Its messages are public and short. The majority of this data is open for all to examine (Vieweg, 2010).