Design of a Government Collaboration Service Map by Big Data Analytics

The purpose of this study was to identify the social phenomena of tattoo, which have been of constant interest in our society, through analysis of social networks collected from big data on what the social phenomena implied in keywords emphasized in newspaper articles over the past year. To this end, by analyzing keywords about tattoos that frequently appeared in newspaper articles, we could see what the main interests of social phenomena related to tattoos were. Data on tattoos were collected from newspaper articles over the past year and analyzed how they formed meaning regarding the relationship structure and centrality between the keywords at issue through social network analysis. These findings provide basic data on social discussions and policy directions related to tattoos in practice and discussions related to ways to improve them. This study is an extension from existing quantitative research by analyzing the social phenomena of tattoos through Bigdata and social network analysis. Apart from statistical surveys or subjective qualitative research, we have approached them with content analysis using big data and social network analysis. The conclusion of this study is as follows. First, as a result of analyzing the word cloud regarding tattoos, it was confirmed that “rose” and “300” were the most prominent, and there were keywords that could analyze various other social phenomena. Second, as a result of analysis by connection centrality, it was proved that the social interest and popularity of tattoos increased. Third, as a result of analysis by eigenvector centrality, the popularity of tattoos was proved. It objectified academic research by attempting research from a different perspective from the analysis of research trends and provided visualized research results of readers.

Understanding barriers to collaborative governance for the food-energy-water nexus: The case of Phoenix, Arizona

Big data poses new challenges to query answering, from computational complexity theory to query evaluation techniques. Several questions arise. What query classes can be considered tractable in the context of big data? How can we make query answering feasible on big data? What should we do about the quality of the data, the other side of big data? This paper aims to provide an overview of recent advances in tackling these questions, using social network analysis as an example.

This study aims to predict the characteristics of the exercise healthcare industry in the post-pandemic era by comparing the periods before and after the coronavirus disease 2019 outbreak through big data analysis. TEXTOM, the Korean big data collection and analysis solution, was used for data collection. The pre-pandemic period was defined as 1 January 2018-31 December 2019 and the pandemic period as 1 January 2020-31 December 2021. The keywords for data collection were "exercise + healthcare + industry". Text mining and social network analysis were conducted to determine the overall characteristics of the Korean exercise healthcare industry. We identified 30 terms that appeared most frequently on social media. Four common (smart management, future technology, fitness, and research) and six different clusters (sports education, exercise leader, rehabilitation, services, business, and COVID-19) were obtained for the pre-pandemic and pandemic periods. Smart management, future technology, fitness, and research are still important values across both periods. The results provide meaningful data and offer valuable insights to explore the changing trends in exercise healthcare.

In recent years, the study of complex networks has attracted great attention. Several fields of science have used techniques of social network analysis and complex networks to represent a wide range of structures such as, social networks, political influence, communication, epidemics and several other aspects of human behavior. Most of the complex networks show community structures. Revealing these communities is highly relevant to understanding several social phenomena such as the organizing of groups, the flow of information and the strength of the influence of some members over the group. In this article, we use techiques of social network analysis and complex networks to represent the relationship between companies that are participating in public bids to unveil community structures analog to cartels. Several nations are facing injuries trough the misuse of public money caused by the formation of cartels, which are groupings of companies aiming to defraud the free competition. Our main goal in this work is to present a methodology for identifying these communities. Furthermore, we aim to address whether companies that have high success rates in public bids are grouped and identify whether they are taking advantage of their influence in the network.

Modelling and analysis of big data platform group adoption behaviour based on social network analysis

Big data is a new phenomenon that force organization to find new way to process, to save, and to analyze data in various forms. Big data challenge is getting bigger and faster but also the opportunities of harvesting information from it. Organization prefers using big data's technology for many advantages, mainly because it is cheaper, effective, efficient and faster than previous system. In some research area, there are conflicting approach to solve the problem by using legacy methodology or by using big data approach. One of the fastest methodology in big data approach is Social Network Analysis. One of research area measured as the research object in this paper is information dissemination.

Predicting diffusions on big social media data using natural language processing (NLP) and social network analysis (SNA) techniques is an emerging research domain. To predict diffusions of novel topics, previous studies focus on predicting the diffusions on cross-topic-observed diffusions (the diffusions between the source and target user of the diffusion are not observed for the topic to be predicted, but still observed for other topics). However, in real world social network, many diffusions to be predicted are actually unobserved. For example, the diffusions may be unseen (the diffusions between the source and target user of the diffusion are not observed in training data), or even with silence users (one or both of the users of the diffusion never participate a diffusion before). In this paper, we generalize the diffusion prediction on novel topic problem to predict both cross-topic-observed and unobserved diffusions, which is very challenging because of lacking previous diffusion records. We design a learning-based framework to solve the problem. Leveraging NLP and SNA techniques to deal with such Big Data, we exploit the latent semantic derived from diverse information sources (e.g., user, topic, user-topic, and topological), and utilize the idea that "users with the same attribute value tend to have similar behavior for similar topics", to extract features for prediction. Our framework is evaluated on real-world microblog data, and the experiments show that we can achieve 73% AUC in this difficult prediction task. Our dataset is also publicly available at http://mslab.csie.ntu.edu.tw/~tim/ase_big_data_2015.zip.

In recent years, vast and complex amounts of data are being created and making it difficult for traditional data processing applications to manage them. The coming of the Internet prompted monstrous spike in the volume of information being made and made accessible. World Wide Web consortium W3C and international standardization body of the web spread the Semantic Web. It is an extended form of current web which provide easier way to search, reuse, combine and share information. In the last few years, major businesses corporations have demonstrated interest in incorporating semantic web technology with big data for added value. Indeed this incorporation has some benefits as well; it increases end-users ability to self-manage data from various sources, it on focuses changing business environments and varying user needs and handles concepts and relationships, manages terminology while connecting different data from varied data sources. For Social Network Analysis (SNA) new methods are needed by combining Big Data and Semantic Web technologies as a way to utilize and add capacities to existing frameworks. Moreover, the fast changing business requirements and latest industry culture of Agile Development needs a robust yet flexible solution for Business Intelligence and by using distributed enterprise level ontologies Data Warehousing can be incorporated. This paper is an attempt to focus on effects of incorporating Big Data with Semantic web, how Semantic Web making Big Data smarter, revisit the Big Data and Semantic Web challenges and opportunities, relationship between them and finally we summarizes with future direction of this integration

Social network analysis (SNA) is a multidisciplinary field dedicated to the analysis and modeling of relations and diffusion processes among various objects in nature and society, and other information/knowledge processing entities with an aim of understanding how the behavior of individuals and their interactions translates into large‐scale social phenomenon. Because of exploding popularity of online social networks and availability of huge amount of user‐generated content, there is a great opportunity to analyze social networks and their dynamics at resolutions and levels not seen before. This has resulted in a significant increase in research literature at the intersection of the computing and social sciences leading to several techniques for social network modeling and analysis in the area of machine learning and data mining. Some of the current challenges in the analysis of large‐scale social network data include social network modeling and representation, link mining, sentiment analysis, semanticSNA, information diffusion, viral marketing, and influential node mining.WIREs Data Mining Knowl Discov2013, 3:408–444. doi: 10.1002/widm.1105This article is categorized under:Algorithmic Development > Web MiningCommercial, Legal, and Ethical Issues > Social ConsiderationsTechnologies > Structure Discovery and Clustering

Sustainable urban waste management increasingly relies on collaborative governance models that engage multiple stakeholders across sectors. However, such collaboration often encounters challenges arising from unequal power distribution, conflicting interests, and fragmented coordination. This study highlights how power imbalances and fragmented coordination not only affect governance efficiency but also impede the achievement of environmental sustainability goals, such as waste reduction and circular resource flows. Adopting a quantitative survey design combined with qualitative in-depth interviews, Social Network Analysis was employed using UCINET 6 software to measure actor centrality through key indicators. The findings demonstrate that actors with formal authority and high legitimacy, particularly municipal environmental departments and established waste banks, dominate the collaborative network. In contrast, community and academic actors, despite having strong interests, often remain structurally peripheral and underrepresented in decision-making processes. The network density value of 0.5220 indicates a moderate level of connectivity, yet the presence of coordination gaps suggests room for improvement in actor engagement and information flow. This study advances the understanding of power and interest configurations in collaborative environmental governance. It also offers practical recommendations for strengthening cross-sectoral coordination and promoting inclusive, stakeholder-driven approaches to waste management that align with sustainable development goals in urban settings.

This study explores how to use big data analysis technology to conduct a comprehensive, objective and in-depth assessment of the communication effect of China’s foreign discourse culture, with a view to optimizing the communication strategy and enhancing the effectiveness of international communication. This study systematically comprehends the theory of foreign discourse culture and soft power, the application of big data technology in the field of cultural communication, the current status of domestic and international research on the assessment of the communication effect of foreign discourse culture, as well as the construction of the theoretical framework for the assessment of the communication effect of foreign discourse culture based on big data and the setting of the research hypotheses. This study collects big data resources from various Internet media platforms such as social media, news media reports, international public opinion databases, online forums and blogs, and carries out data cleansing and standardization to lay a data foundation for the subsequent analysis. This study adopts two major data mining techniques, text mining and social network analysis, as well as a variety of data mining algorithms, such as topic modeling, sentiment analysis, and influence calculation, to conduct in-depth mining and analysis of the data on the cultural communication of foreign discourses, and develops a communication path recognition system for tracking and recording the trajectory of the dissemination of foreign discourses and constructing a communication network mapping. In addition, this paper demonstrates the effect of the model through specific cases. It is found that through the deep mining of big data, we can intuitively see the degree of influence of different discourse contents, communication channels, time nodes, and other factors on the communication effect, thus providing a scientific basis and decision-making support for policy makers and communication practitioners.

Digital technologies such as the Internet, big data, cloud computing, mobile communication, and artificial intelligence have played an important role in the prevention and control of this new crown epidemic. Driven by big data technology, collaborative governance in the digital era has injected new dynamics for collaborative governance in the general sense, especially in this new crown epidemic prevention and control shows a different governance logic from the past, and the refinement and extraction of this governance logic is the key to enhance the ability of new crown epidemic prevention and control. Compared with the previous model in which a single governmental governance body played a role. In this paper, the necessity of building a big data analysis system is clarified by analyzing the limitations of the data analysis means of the existing trip code system. The overall epidemic prevention advantage of adopting the big data trip code policy has increased by 51.95% for regions with high crisis risk level and strong economic power, such as Guangdong, Zhejiang and Shanghai, which need to face the risk spread from domestic and imported risks under the big data trip code control, the increase of new cases compared to the previous comparison has decreased by 68.5%, which can visually see the big data trip code epidemic prevention We can visually see the effectiveness of big data trip codes in preventing the epidemic and making excellent contributions to the global fight against the epidemic.

Most social phenomena are inherently complex and hard to measure, often due to under-reporting, stigma, social desirability bias, and rapidly changing external circumstances. This is for instance the case of Intimate Partner Violence (IPV), a highly-prevalent social phenomenon which has drastically risen in the wake of the COVID-19 pandemic. This paper explores whether big data—an increasingly common tool to track, nowcast, and forecast social phenomena in close-to-real time—might help track and understand IPV dynamics. We leverage online data from Google Trends to explore whether online searches might help reach “hard-to-reach” populations such as victims of IPV using Italy as a case-study. We ask the following questions: Can digital traces help predict instances of IPV—both potential threat and actual violent cases—in Italy? Is their predictive power weaker or stronger in the aftermath of crises such as COVID-19? Our results suggest that online searches using selected keywords measuring different facets of IPV are a powerful tool to track potential threats of IPV before and during global-level crises such as the current COVID-19 pandemic, with stronger predictive power post outbreaks. Conversely, online searches help predict actual violence only in post-outbreak scenarios. Our findings, validated by a Facebook survey, also highlight the important role that socioeconomic status (SES) plays in shaping online search behavior, thus shedding new light on the role played by third-level digital divides in determining the predictive power of digital traces. More specifically, they suggest that forecasting might be more reliable among high-SES population strata.

Twitter Big Data ((( ) (Message Propagation Analysis in Twitter Using Arrays of Big Data: The Case of Russia's Image in the Arctic)