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The purpose of this paper is to provide an overview of NodeXL in the context of news diffusion. Journalists often include a social media dimension in their stories but lack the tools to get digital photos of the virtual crowds about which they write. NodeXL is an easy to use tool for collecting, analysing, visualising and reporting on the patterns found in collections of connections in streams of social media. With a network map patterns emerge that highlight key people, groups, divisions and bridges, themes and related resources.

This study conducts a literature review of previous empirical work which has utilised NodeXL and highlights the potential of NodeXL to provide network insights of virtual crowds during emerging news events. It then develops a number of guidelines which can be utilised by news media teams to measure and map information diffusion during emerging news events.

One emergent software application known as NodeXL has allowed journalists to take “group photos” of the connections among a group of users on social media. It was found that a diverse range of disciplines utilise NodeXL in academic research. Furthermore, based on the features of NodeXL, a number of guidelines were developed which provide insight into how to measure and map emerging news events on Twitter.

With a set of social media network images a journalist can cover a set of social media content streams and quickly grasp “situational awareness” of the shape of the crowd. Since social media popular support is often cited but not documented, NodeXL social media network maps can help journalists quickly document the social landscape utilising an innovative approach.

This is the first empirical study to review literature on NodeXL, and to provide insight into the value of network visualisations and analytics for the news media domain. Moreover, it is the first empirical study to develop guidelines that will act as a valuable resource for newsrooms looking to acquire insight into emerging news events from the stream of social media posts. In the era of fake news and automated accounts, i.e., bots the ability to highlight opinion leaders and ascertain their allegiances will be of importance in today’s news climate.

The purpose of this paper is to demonstrate novel techniques for exploring relationship data extracted from social media sites for actionable insights by educators, researchers, and administrators.

The paper demonstrates how non‐programmers can use NodeXL, an open source social network analysis tool built into Excel 2007/2010, to collect, analyze, and visualize network data from social media sites like Twitter and YouTube.

Researchers and education professionals can use NodeXL to explore (a) social networks to identify important individuals and subgroups, as well as (b) content networks to map the underlying structure of a domain and find important content. Illustrative examples are provided using NodeXL to examine followers of a Twitter user focused on open education, as well as a content network of YouTube videos about surgery.

Tools like NodeXL are making network analysis accessible to non‐technical researchers in a variety of fields spanning the sciences, social sciences, and the humanities. Despite their value, network analysis techniques are only as good as the data that underlie them, requiring careful assessment of possible selection biases and triangulation of findings.

Educational institutions and educators can benefit from more systematically analyzing their social media initiatives from a network perspective.

This paper describes some of the techniques and tools needed to make sense of the social relationships that underlie social media sites. As relational data are increasingly made public, such techniques will enable more systematic analysis by researchers studying social phenomena and practitioners implementing social media initiatives.

This chapter seeks to understand the intersection between eGovernment, social media, and digital inequalities by examining the disparate flow of information during the COVID-19 pandemic. Developed economies are increasingly transitioning to digital interfaces for information dissemination and provision of services. The authors explore the potential of, and challenges facing eGovernment by looking at the use of social media during the COVID-19 pandemic. This chapter employs a case study approach to probe the dynamics of government-initiated efforts at information dissemination through the Center for Disease Control and Prevention’s (CDC) website and social media account on Twitter. The analysis in this chapter uses NodeXL to examine communication roles played by government and non-governmental actors within this slice of the Twittersphere centered around CDC@gov. As the findings demonstrate, non-governmental actors played key roles in the dissemination of public health messaging. The authors analyze these data with an eye to the potential of social media for public health communication and extrapolate that understanding to the use of digital access and social media for the provision of accurate, official information in other circumstances. While the COVID-19 pandemic was a global health crisis, individuals and households face individual or local crises every day. This angle of vision allows the chapter to conclude with recommendations pertaining to government-led information dissemination for the public good during crisis and non-crisis situations alike. In the concluding section, the authors probe the degree to which eGovernment can also address digital inequalities including connectivity, device, and literacy gaps. The authors offer solutions needed for eGovernment initiatives in light of challenges posed by digital inequalities to ensure that digital information sharing and services are accessible to all.

The purpose of this paper is to use the Twitter Search Network of the Apache NodeXL data discovery tool to extract over 5,000 data from Twitter accounts that twitted, re-twitted or commented on the hashtag, #NigeriaDecides, to gain insight into the impact of the social media on the politics and administration of developing countries.

Several algorithms like the Fruchterman-Reingold algorithm, Harel-Koren Fast Multiscale algorithm and the Clauset-Newman-Moore algorithms are used to analyse the social media metrics like betweenness, closeness centralities, etc., and visualize the sociograms.

Results from a typical application of this tool, on the Nigeria general election of 2015, show the social media as the major influencer and the contribution of the social media data analytics in predicting trends that may influence developing economies.

With this type of work, stakeholders can make informed decisions based on predictions that can yield high degree of accuracy as this case. It is also important to stress that this work can be reproduced for any other part of the world, as it is not limited to developing countries or Nigeria in particular or it is limited to the field of politics.

Increasingly, during the 2015 general election, citizens have taken over the blogosphere by writing, commenting and reporting about different issues from politics, society, human rights, disasters, contestants, attacks and other community-related issues. One of such instances is the #NigeriaDecides network on Twitter. The effect of these showed in the opinion polls organized by the various interest groups and media houses which were all in favour of GMB.

The case study the authors took on the Nigeria’s general election of 2015 further strengthens the fact that the developing countries have joined the social media race. The major contributions of this work are that policy makers, politicians, business managers, etc. can use the methods shown in this work to harness and gain insights from Big Data, like the social media data.

This study looks at how local grassroots organizations as well as international Women Non-Governmental Organizations (WNGOs) and multilateral organizations such as the United Nations utilize social media to empower women in Jamaica and Brazil. The researchers also evaluate how issues of socio-economic background as well as social media infrastructure influence the selection of entities with which the respective WNGOS connect.

This study uses NodeXL, a social media research tool, to analyze the information found on WNGO social media pages such as Facebook and Twitter. The authors also use content analysis to make sense of the data on WNGO Facebook pages. The study specifically uses summative content analysis, a method that translates the frequency of occurrence of certain symbols into summary judgments and comparisons.

Social media usage by WNGOs in Jamaica and Brazil show striking similarities regarding who gets reached or are connected to the networks. The study reveals that women of lower socio-economic backgrounds in both cases are not being reached via social media. Further, the outcomes of the observed current social media communication patterns on WNGO social media sites suggest the occurrence of what the authors refer to as the “noticeboard” effect, wherein communication patterns are top-down, exclusive, and non-reciprocal in nature.

While social media offer less centralized ways of engaging in communication with local communities, inherent in social media infrastructure are issues of race, gender, and social class that affect how these communication platforms are used, potentially another dimension of the “Mathew Effect” in the context of social media usage for purposes of achieving national development objectives.

With the rise in internet penetration in both countries, WNGOs are increasingly incorporating social media into their communication strategies to accomplish development goals. This study is the first to compare both countries in this respect and so adds new insights to this area of the communication field.

Social network analysis (SNA) may be useful in the exploration of the potential intersection between organizations, human relations (HR), public relations (PR), and corporate social responsibility (CSR) or sustainability issues. In an age of PR, social media have an important function, as reflected in the employee engagement movement. Visualization through mapping of Twitter data allows researchers to identify centers of social influence in which content travels through key accounts during sharing. Computer-mediated communication (CMC) concepts of identity, interaction, and community offer potential for future research.

The study aims to explore the digital fashion trend within the Metaverse, characterized by non-fungible tokens (NFTs), across Twitter networks. Integrating theories of diffusion of innovation, two-step flow of communication and self-efficacy, the authors aimed to uncover the diffusion structure and the influencer's social roles undertaken by social entities in fostering communication and collaboration for the advancement of Metaverse fashion.

Social network analysis examined the critical graph metrics to profile, visualize, and cluster the unstructured network data. The authors used the NodeXL program to analyze two hashtag keyword networks, “#metaverse fashion” and “#metawear,” using Twitter API data. Cluster, semantic, and time series analyses were performed to visualize the contents and contexts of communication and collaboration in the diffusion of Metaverse fashion.

The results unraveled the “broadcast network” structure and the influencers' social roles of opinion leaders and market mavens within Twitter's “#metaverse fashion” diffusion. The roles of innovators and early adopters among influencers were comparable in collaborating within the competition venues, promoting awareness and participation in digital fashion diffusion during specific “fad” periods, particularly when digital fashion NFTs and cryptocurrencies became intertwined with the competition in the Metaverse.

The study contributed to theory building by integrating three theories, emphasizing effective communication and collaboration among influencers, organizations, and competition venues in broadcasting digital fashion within shared networks. The validation of multi-faceted Social Network Analysis was crucial for timely insights, highlighting the critical digital fashion equity in capturing consumers' attention and driving engagement and ownership of Metaverse fashion.

Purpose: Forces of fragmentation make achieving climate change goals difficult. Social media is enabling more stakeholders to get involved in sustainability debates. This study aimed to investigate the debates taking place about sustainability on Twitter using the hashtag #ClimateAction as a search term, to identify influential actors and their connections with other users on the platform.

Methodology: NodeXL was used to investigate the social network structure of actors discussing #ClimateAction on Twitter. Tweets were thematically used to identify the topics being discussed. The most influential actors involved in the network were identified. The tweets included in the analysis were posted between Saturday 17 August and Thursday 22 August 2019. The data set was obtained from Twitter on 22 August 2019 and was downloaded from NodeXL Graph Gallery.

Findings: The network included 14,512 Twitter users whose recent tweets contained #ClimateAction, or who were replied to or mentioned in those tweets. The network included 38,855 unique edges with an average geodesic distance of 4.19, indicating a close network. Five key themes were identified: Encouraging action, Citizen movements and activists, Climate change and its effects on the planet, Politics, policies and approaches for climate change and Climate research.

Originality: The study provides new qualitative insights into how Twitter is used as a platform for debate and agenda setting for sustainability. The debate was not as polarized as previous research suggests which may reflect the global nature of Twitter and the increased capacity for advocates to shape policy debates about sustainability on social media.

The purpose of this paper is to apply Connolly’s (2003) concept of agonistic respect to develop a typology of agonistic/antagonistic discourses on Twitter. To develop the typology, this study examines 2,236 Tweets containing the hashtag #guncontrol and uses NodeXL (Smith et al., 2010) to create a network map from which the 75 most influential accounts are derived. Using constant-comparative analysis (Glaser and Strauss, 1967), the authors identify seven categories of discourse style based on Connoly’s (2001) notion of ressentiment and “good faith presentations” of opposing arguments: furtive/secretive, cravenly opportunistic, willfully ignorant, irrational sentimental, misunderstanding/misguided, contingently wrong and reciprocal inquiry. The typology provides a useful and unique way to operationalize agonistic democratic theory and serves as the possible basis for training a machine learning classifier to detect antagonistic discourses on social media platforms.

To determine the level of agonism on Twitter, the authors examine tweets that employed the hashtag #guncontrol on March 12, 2018, one month after the shooting at the Marjory Stoneman Douglass High School in Parkland, Florida on February 14. The authors used the NodeXL excel add-on to collect and map 2,236 tweets. Using grounded theory/constant-comparative analysis (Glaser and Strauss, 1967), the authors develop a typology of seven types of discourses ordered from most antagonistic to most agonistic using Connolly’s (1993) concept of agonistic respect.

After examining the top 75 most shared tweets and using constant-comparative analysis to look for patterns of similarity and dissimilarity, the authors identified seven different ways in which individuals present their opponents’ value positions on Twitter on the issue of gun control. The authors were guided by agonistic theory in the authors’ inquiry. The authors looked at how Twitter users expressed their opponent’s faith/value positions, how pluralistic the discourse space was in the comment threads and how much the “talk” was likely to elicit ressentiment from adversaries.

Because the authors intended to engage in theory building, the authors limited the analysis to a selected number of tweets on one particularly salient topic, on one day. The intent of this was to allow for a close reading of the tweets in that specific network for the purposes of creating a useful typology that can be applied to a broader range of cases/issues/platforms.

The authors hope that typology could serve as a potential starting point for Twitter to think about how it could design its algorithms toward agonistic talk. The typology could be used as a classification scheme to differentiate agonistic from antagonistic threads. An algorithm could be trained to spot threads overwhelmingly populated by antagonistic discourse and instructed to insert posts from other threads that represent agonistic responses like “contingently wrong” or “reciprocal inquiry.” While generous presentations or deeper, more nuanced presentations of the opponent’s value position are not a panacea, they could serve to change the orientation by which users engage with policy issues.

Social media platforms like Twitter have up to now been left alone to make markets and establish profitability off of public sphere conversations. The result has been a lack of attention to how discourse on these platforms affects users mental well-being, community health and democratic viability. Recently, Twitter’s CEO has indicated a need to rethink the ways in which it promotes “healthy discourse.” The utilitarian presumption that, left to our own devices, we will trial and error our way to the collective good does not account for the importance of others in refining one’s preferences, arguments and world views. Without an “other” to vet ideas and lead us toward becoming wiser, we are left with a Wyly antagonism that moves discourse further and further away from agonistic respect and toward antagonistic virtual struggle. Platforms that allow antagonistic talk that breeds ressentiment run the risk of irrevocably damaging democracy through poisoning its public sphere.

This paper is unique in providing a typology/framework for thinking about the types of “political talk” that exists on Twitter. By using agonistic political theory as a framework, the authors are able to establish some guiding principles for “good political talk” that acknowledges the incommensurability of value positions on issues like gun control. The typology’s emphasis on agonistic respect, ressentiment and generosity in the presentation of alternative value positions provides a starting point from which to map and catalog discourse on Twitter more generally and offers a normative model for changing algorithmic design.