Daily data on the use of the world’s 425 most frequently visited websites by 118 countries were mined over the 4-month period September 1 to December 31, 2015, to create a longitudinal two-mode network (countries and websites). This paper describes the changes in the international World Wide Web (WWW), as a network, to determine the effects of unanticipated shocks (the terrorist attacks on Paris and San Bernardino) and predictable events [national holidays (Golden Week, Christmas, New Years) and shopping days, Black Friday and 11/11]. The results indicate that while there are changes in the use of individual websites, due to weekly cycles in viewing specific websites, the shocks and other social and cultural events, the overall network is remarkably stable. This resilience is due to the constraints that network ties place on the relationships among the websites, which limits their potential behavior.

This study describes the structure of the international Facebook friendship network and its determinants using various predictors, including physical proximity, cultural homophily, and communication. Network analysis resulted in one group of nations, with countries that bridge geographic and linguistic clusters (France, Spain, United Kingdom, and United Arab Emirates) being the most central. Countries with international Facebook friendship ties tended to share borders, language, civilization, and migration. Physical distance, shared hyperlinks, use of common websites, telephone traffic, cultural similarity, and international student exchange were either weakly or not significantly related to international Facebook friendships.

This research examines who controls the international Internet using a multi-level network analysis. Internet provider (IP) address ownership data obtained from TeleGeography (http://www.telegeography.com/) are used to describe the structure of the international network of private companies and Internet service providers (ISPs). The results indicate that 16 companies, headquartered in seven countries, are at the center of the network, each accounting for more than 1% of international Internet connections. Level 3 Communications, based in the United States, is at the center of the network. The United States is the most central nation in the ownership network. American corporations account for almost 40% of the international links. A cluster analysis found a single group centered about the United States. The ownership network is consistent with other measures of the international Internet network. Its structure replicates and maintains the global hierarchy as suggested by world system and globalization theory. Policy implications are discussed.

This paper explores the use of spectral or frequency domain analysis to examine network evolution and co-evolution. Given a sufficient number of measurements of a valued network over-time, a matrix (Κ) may be created, where κij is the squared coherency between nodes i and j. Since Κ is a matrix of relations, it may be analyzed like any other network to determine which nodes co-evolve, and which change independently. To illustrate this approach, cross-spectral analyses were conducted among 250 pairs of nation-states based on terrorism news coverage (2000–2012) to create matrix Κ. Then, a network analysis was conducted. The results indicate that the United States was the most central country, and that the nations co-evolved in two large groups (the Middle East and East Asia), and numerous smaller ones. The proposed method is extended to include exogenous variables (terrorist events and oil prices) and to two or more networks to examine network co-evolution. The method’s application to network co-evolution is demonstrated with additional data on networks of co-association in terrorism news coverage (2011–2013) and a comparable data set on the coverage of the Arab Spring (2011–2013).

A semantic network analysis was conducted to investigate how national political culture shapes news frames for international political coverage. Specifically, the Associated Press framed the Arab Spring as a process of pursuing democracy, a core value of American political culture, whereas Xinhua News Agency framed it as a crisis that challenged authority and the related stability, the central concerns of Chinese political culture. The results of the analysis of coevolution of cultural symbols indicate that competition between the two different cultural frames happened with the purpose of negotiating the meaning of the Arab Spring on the global stage.

Social media communication is characterized by reduced anonymity and off-to-online social interactions. These characteristics require scholars to revisit social influence mechanisms online. The current study builds on social influence literature to explore social network and gender effects on online behavior. Findings from a quasi-experiment suggest that both network-related variables and gender are significantly associated with online behavior. Perceived social environment, measured by personal network exposure rate, is more significant than objective reality, measured by frequency of received social messages, in determining behavior. We discuss the implications of social contagion effects on web-based strategic communication-including advertising, political campaigns, and social mobilization. Data limitations and the difficulty of measuring social network influence via social media are also discussed. © 2014 SAGE Publications.

The science of social network analysis has co-evolved with the development of online environments and computer-mediated communication. Unique and precise data available from computer and information systems have allowed network scientists to explore novel social phenomena and develop new methods. Additionally, advances in the structural analysis and visualization of computer-mediated social networks have informed developers and shaped the design of social media tools. This article reviews some examples of research that highlight the ways that social network analysis has evolved with online data. Examples include the international hyperlink network, political blogs and hyperlinks, social media, and multi-user virtual environments. The data available from online environments makes several important contributions to network science, including reliable network flow data, unique forms of relational data across a myriad of contexts, and dynamic data allowing for longitudinal analysis and the animation of social networks.

Scholars have posed different hypotheses on the impact of global telecommunications on value orientations. We analyze and characterize the global telecommunication network and test a series of hypotheses on the relationship between gender values and three types of telephone connections: ties with the global society, ties with Western nations, and ties within groups of nations sharing similar cultural, religious, political, or geographical traits. We use multilevel models and data on two levels, between-country telecommunications network data from TeleGeography, and individual-level data (N = 70,225) on people living in 47 countries from the World Value Survey, waves III and IV. Countries with high degrees of communication insulation, measured as a high percentage of within-group ties of all global telephone links, hold less egalitarian attitudes toward gender equality. This negative effect of group insulation depresses the egalitarian effects of younger birth cohort, college education, and higher income. Embeddedness in a localized information diffusion network and isolated from global communication is associated with less egalitarian attitude toward gender equality. But neither global ties nor ties with Western countries are linked with gender attitudes.

This study compares three different methods commonly employed for the determination and interpretation of the subject matter of large corpuses of textual data. The methods reviewed are: (1) topic modeling, (2) community or group detection, and (3) cluster analysis of semantic networks. Two different datasets related to health topics were gathered from Twitter posts to compare the methods. The first dataset includes 16,138 original tweets concerning HIV pre-exposure prophylaxis (PrEP) from April 3, 2019 to April 3, 2020. The second dataset is comprised of 12,613 tweets about childhood vaccination from July 1, 2018 to October 15, 2018. Our findings suggest that the separate "topics" suggested by semantic networks (community detection) and/or cluster analysis (Ward's method) are more clearly identified than the topic modeling results. Topic modeling produced more subjects, but these tended to overlap. This study offers a better understanding of how results may vary based on method to determine subject matter chosen.

Making sound food and agriculture decisions is important for global society and the environment. Experts tend to view crop genetic engineering, a technology that can improve yields and minimize impacts on the environment, more favorably than the public. Because there is a causal relationship between public opinion and public policy, it is important to understand how opinions about genetically engineered (GE) crops are influenced. The public increasingly seeks science information on the Internet. Here, semantic network analysis is performed to characterize the presentation of the term "GMO (genetically modified organism)," a proxy for food developed from GE crops, on the web. Texts from three sources are analyzed: U.S. federal websites, top pages from a Google search, and online news titles. We found that the framing and sentiment (positive, neutral, or negative attitudes) of "GMO" varies across these sources. It is described how differences in the portrayal of GE food by each source might affect public opinion. A current understanding of the types of information individuals may encounter online can provide insight into public opinion toward GE food. In turn, this knowledge can guide teaching and communication efforts by the scientific community to promote informed decision-making about agricultural biotechnologies.