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Analyzing Social Media Networks with NodeXL offers backgrounds in information studies, computer science, and sociology.This book is divided into three parts: analyzing social media, NodeXL tutorial, and social-media network analysis case studies.Part I provides background in the history and concepts of social media and social networks.

Social media data reveal patterns of knowledge, attitudes, and behaviours of users on a range of topics. This study analysed 4398 tweets gathered between 17 January 2022 and 3 February 2022 related to ecosystem services, using the keyword and hashtag “ecosystem services”. The Microsoft Excel plugin, NodeXL was used for social and semantic network analysis. The results reveal a loosely dense network in which information is conveyed slowly, with homogeneous, medium-sized subgroups typical of the community cluster structure. Citizens, NGOs, and governmental administrations emerged as the main gatekeepers of information in the network. Various semantic themes emerged such as the protection of natural capital for the sustainable production of ecosystem services; nature-based solutions to protect human structures and wellbeing against natural hazards; socio-ecological systems as the interaction between human beings and the environment; focus on specific services such as the storage of atmospheric CO2 and the provision of food. In conclusion, the perception of social users of the role of ecosystem services can help policymakers and forest managers to outline and implement efficient forest management strategies and plans.

The digital landscape has become a vital platform for public health discourse, particularly concerning important topics like organ donation. With a global rise in organ transplant needs, fostering public understanding and positive attitudes toward organ donation is critical. Social media platforms, such as X, contain conversations from the public, and key stakeholders maintain an active presence on the platform. The goal is to develop insights into organ donation discussions on a popular social media platform (X) and understand the context in which users discussed organ donation advocacy. We investigate the influence of prominent profiles on X and meta-level accounts, including those seeking health information. We use credibility theory to explore the construction and impact of credibility within social media contexts in organ donation discussions. Data were retrieved from X between October 2023 and May 2024, covering a 7-month period. The study was able to retrieve a dataset with 20,124 unique users and 33,830 posts. The posts were analyzed using social network analysis and qualitative thematic analysis. NodeXL Pro was used to retrieve and analyze the data, and a network visualization was created by drawing upon the Clauset-Newman-Moore cluster algorithm and the Harel-Koren Fast Multiscale layout algorithm. This analysis reveals an \"elite tier\" shaping the conversation, with themes reflecting existing societal sensitivities around organ donation. We demonstrate how prominent social media profiles act as information intermediaries, navigating the tension between open dialogue and negative perceptions. We use our findings, social credibility theory, and review of existing literature to develop the OrgReach Social Media Marketing Strategy for Organ Donation Awareness. The OrgReach strategy developed is based on 5 C's (Create, Connect, Collaborate, Correct, and Curate), 2 A's (Access and Analyse), and 3 R's (Recognize, Respond, and Reevaluate). The study highlights the crucial role of analyzing social media data by drawing upon social networks and topic analysis to understand influence and network communication patterns. By doing so, the study proposes the OrgReach strategy that can feed into the marketing strategies for organ donation outreach and awareness.

User generated content in the social media platforms are being considered as an important source for information about consumers and other emerging trends by the businesses. Using Twitter analytics, the paper presents insights on trends and discussions about the Internet of Things (IoT). Using relevant hashtags, 40,387 tweets were collected in early 2016. The analysis had followed three major approaches: descriptive analysis, content analysis and network analysis. The tools R and NodeXL were used for the analysis. The findings showed major themes like business concerns, scope of applications, security, emerging smart technologies and manufacturing. The sentiments of emotions and polarity differed across these themes. The top individual and industrial influencers were identified. The analysis also detected the highly-associated words and hashtags, and different user communities and how they are connected. Business implications of the findings and limitations are also elaborated.

Social paleontology is a burgeoning field of research that seeks to understand the natural world through the collection, preparation, curation, and study of fossils via online communities. Such a community represents an ideal case for examining scientific practice as the expression of conversation topics in relation to the people who participate. Using Communities of Practice as a theoretical framework, we consider interactions within an egocentric Twitter network over a 397-day period to identify topic archetypes within the community, examine how such topic archetypes act as expressions of behavior that are indicative of community processes, and provide empirical evidence for detecting and indicating the health of an online community. Data were collected continuously and analyzed with a combination of topic modeling and social network analysis. Four unique archetypes were characterized based on the level of activity and longevity of interest. Participants for each were diverse, but not different. Structural differences in each network were noted with high levels of inter-group information flow within certain archetypes. Archetypes were interpreted using the life cycle states for Communities of Practice; sustained conversations and piques of interest indicate healthy online communities. These findings can inform efforts to design, implement, and research online, scientific communities.

Characterizing who participates in and contributes to science communication efforts can extend our understanding of the science education ecosystem, including giving insight into what kinds of communication works for whom and under what conditions. We describe methods for characterizing an online, scientific affinity space using @TimeScavengers as our exemplar. @TimeScavengers is an educational and science communication effort centered on geosciences, and uses varied online platforms, including Twitter (now known as X). We applied an established framework to describe members who connected with @TimeScavengers' Twitter (n = 1113) and analyzed data (e.g. tweets, replies, mentions, and re-tweets) (n = 6538) collected from the community for an annual cycle of activity (January 2020-2021) via social network analysis, topic modeling, and diversity indices. Social network analysis showed a highly dispersed network with scientists in control of information flow. Topic modeling of tweets with original content (n = 855) generated six topics related to collaboration, connection, and scientific outreach. Application of diversity indices indicated that scientists and education and outreach members were most prevalent across topics. Using these methods uncovers deeper understanding of the community and interactions within the space, which can lead to the development of better science communication efforts in the digital realm. We looked at who took part in science communication on Twitter, using a specific handle (@TimeScavengers) to understand this. Using different methods, we found that scientists shared most of the information. We identified six main topics in the tweets, mostly about working together, connecting with others, and sharing science. We also found that scientists and people involved in education and outreach were the most active. By studying these online communities, we can improve how science is communicated and engaged with by the public. This helps us understand the best ways to share science online.

The severity and frequency of flood and drought events have increased in recent decades. These climate change-induced and anthropogenic stressors on water resources represent the leading water-related hazards to communities. Further, the increasing exposure of the population and infrastructure to such events has heightened the risks. Assessing the impact scope of these events in different subfields towards comprehensively evaluating the risks requires an unbiased systematic approach. This paper combines content analysis and science mapping to investigate the existing multidisciplinary body of knowledge on analyzing flood and drought together. Searching the literature using selected search terms yielded a sample of 119 publications. Initially, various contents, such as the authors’ keywords, applied methods and indices, and study scale, were extracted from these articles. These contents were then incorporated into the science mapping technique to form communicative networks. Analyzing these publications revealed 13 major research themes, with a sustained focus on hydrological issues. However, a more diverse range of themes was recently revealed, including economy, sociology, insurance, and policymaking. Producing such computational and visual networks explained informative insights that can help further develop both existing and new frameworks to support the management, design and policymaking sectors in responding to both flood and drought events.

Social media platforms provide valuable insights into public conversations. They likewise aid in understanding current issues and events. Twitter has become an important virtual venue where global users hold conversations, share information, and exchange news and research. This study investigates social network structures among Twitter users with regard to the Covid-19 outbreak at its onset and its spread. The data were derived from two Twitter datasets by using a search query, “coronavirus,” on February 28th, 2020, when the coronavirus outbreak was at a relatively early stage. The first dataset is a collection of tweets used in investigating social network structures and for visualization. The second dataset comprises tweets that have citations of scientific research publications regarding coronavirus. The collected data were analyzed to examine numerical indicators of the social network structures, subgroups, influencers, and features regarding research citations. This was also essential to measure the statistical relationships among social elements and research citations. The findings revealed that individuals tend to have conversations with specific people in clusters regarding daily issues on coronavirus without prominent or central voice tweeters. Tweets related to coronavirus were often associated with entertainment, politics, North Korea, and business. During their conversations, the users also responded to and mentioned the U.S. president, the World Health Organization (WHO), celebrities, and news channels. Meanwhile, people shared research articles about the outbreak, including its spread, symptoms related to the disease, and prevention strategies. These findings provide insight into the information sharing behaviors at the onset of the outbreak.

The term deepfake was first used in a Reddit post in 2017 to refer to videos manipulated using artificial intelligence techniques and since then it is becoming easier to create such fake videos. A recent investigation by the cybersecurity company Deeptrace in September 2019 indicated that the number of what is known as fake videos had doubled in the last nine months and that most were pornographic videos used as revenge to harm many women. The report also highlighted the potential of this technology to be used in political campaigns such as in Gabon and Malaysia. In this sense, the phenomenon of deepfake has become a concern for governments because it poses a short-term threat not only to politics, but also for fraud or cyberbullying. The starting point of this research was Twitter’s announcement of a change in its protocols to fight fake news and deepfakes. We have used the Social Network Analysis technique, with visualization as a key component, to analyze the conversation on Twitter about the deepfake phenomenon. NodeXL was used to identify main actors and the network of connections between all these accounts. In addition, the semantic networks of the tweets were analyzed to discover hidden patterns of meaning. The results show that half of the actors who function as bridges in the interactions that shape the network are journalists and media, which is a sign of the concern that this sophisticated form of manipulation generates in this collective.

La crisis sanitaria global surgida por la expansión del Covid-19 ha llevado a la OMS a acuñar el término infodemia para definir una situación de miedo e inseguridad en la que la difusión de información falsa se ha generalizado. Estos bulos se aprovechan de este tipo de emociones para propagarse más rápido que el propio coronavirus, generando a su paso temor y desconfianza en la población. La difusión de estas mentiras, parte de las cuales circula por las redes sociales, resulta peligrosa porque afecta a la salud y puede hacer que se agrave el contagio y provocar la muerte de personas. Esta investigación tiene como objetivo analizar y visualizar la red tejida alrededor de las noticias falsas que circulan en Twitter sobre la pandemia del coronavirus mediante la técnica del análisis de redes sociales. Se ha empleado el software NodeXL Pro. Se han utilizado varias medidas de centralidad para generar la red de conexiones entre los usuarios, representar sus patrones de interacción e identificar los actores clave dentro de la estructura. Además, también se ha creado una red semántica para descubrir las diferencias en la forma en que los grupos de personas hablan sobre el tema. Los resultados muestran que la situación en EUA domina la conversación, pese a que en ese momento apenas registraba casos y Europa se había convertido en el epicentro global del Covid- 19. A pesar de las acusaciones de inacción de periodistas y críticos del gobierno de Trump, se observan varias semanas en las que la desinformación distrae de tomar medidas más eficaces y prevenir verdaderamente el contagio. Además, entre los actores con posiciones más destacadas en la red se constata la escasa presencia de científicos e instituciones que ayuden a desmentir los bulos y expliquen las medidas de higiene.