Computational International Relations What Can Programming, Coding and Internet Research Do for the Discipline?

Digital data on human behaviour and social interactions are a seemingly abundant and valuable resource of the twenty-first century, promising deep insights into the social processes generating them. Such data - commonly referred to as digital trace data, process-generated data, or digital behavioural data - are increasingly available to us, but the goal of unlocking their potential remains elusive despite the emergence of specialized fields such as computational social science, which has produced an ample body of research based on this resource. Achieving that goal requires revisiting the foundations of research based on this data type, a thorough understanding of its unique characteristics, overcoming access barriers, understanding data generating processes, and rethinking the role of theory at the intersection between computer science and social science. This chapter discusses the origin and characteristics of such data, as well as the interdisciplinary challenges researchers face in accessing, understanding, and using it for research.

Big data analytics (BDA) is an advanced analytic technique used with very large and diverse sets of data from different sources. Natural language processing (NLP) is a technology that interfaces with different fields such as computer science, linguistics, and human-computer interactions. Over the past few years, there is a growing number of firms, which are using different BDA and NLP applications in their businesses. Only a few of the research have investigated different dimensions of NLP and BDA and their impacts on the overall organizational performance. There is a growing interest among researchers and practitioners in understanding the consequences for firms that adopt BDA and NLP applications. In this context, the aim of this article is to determine the factors for the usage of BDA and NLP applications in business. With the help of dynamic capability view theory and existing literature, a theoretical model was developed conceptually. Later, the model was validated using structural equation modeling approach considering 1287 samples from 23 firms, primarily based in Asia and Europe, which use NLP and BDA applications. The article finds that NLP and BDA applications help the firms to improve their operational efficiency, which in turn improves the overall firm performance.

The Hypertext 2015 Doctorial Consortium session is held the first day of the 26th ACM Conference on Hypertext and Social Media. It offers Ph.D. students an opportunity to present their ongoing research towards obtaining a Ph.D. degree in the disciplines related to the conference, mostly in Computer and Information Sciences but not limited to them. The themes of the submissions are strongly connected to the research tracks of this year's conference: Digital Connectivity, Data Connectivity and Digital Humanities. The Digital Connectivity track targets developing insights into the mechanisms of information generation and dissemination, characterization of evolutionary processes on online social networks, and studies of models and systems that support these processes. The second track, Data Connectivity, deals with the methods, techniques and technologies that can be used to make data available on the Web, with a special focus on how heterogeneous data sources can be connected to each other. Finally, the track of Digital Humanities seeks to attract work from an interdisciplinary perspective, on the intersection between computer science on one hand, and the humanities and social sciences on the other. During the DC session, students receive constructive feedback from their peers and from a panel of mentors to support them in envisioning added-value contributions to the state-of-the-art research in Hypertext and Social Media. The consortium session is open to all doctorial students by application. Each submission was reviewed by three senior researchers in the topics the students made their submissions. The DC session is aimed in particular at students who have defined a dissertation topic but are still more than one year from graduating at the time of application in order to obtain benefits from feedback. This year, the committee has accepted three contributions to be presented during the Doctorial Consortium session: Automated Methods for Identity Resolution in Heterogeneous Social Platforms by Paridhi Jahin. In this work, connected to the digital and data connectivity tracks of the conference, the author proposes novel methods to search and link user identities scattered across heterogeneous social networks. The methods consider carefully users' privacy, so they are designed to access only public and historic data. The evaluation is proposed on large datasets over multiple platforms to prove their significance in identity resolution of an online user. Language Innovation and Change in On-line Social Networks by Daniel Kershaw. As a fundamental aspect for human communication, this research focuses on forecasting online language change through the use of predictive and descriptive methodologies. This work is framed within structuration theory which helps the researcher in structuring the analysis of the dynamics of language (re)production - i.e. by the agent (user), the social structure and their interplay. A Framework to Provide Customized Reuse of Open Corpus Content for Adaptive Systems by Mostafa Bayomi. This work deals with issues of reusability of contents on adaptive systems. Adaptive systems tailor content specific to user's needs, but they face two big problems: (a) they use a closed corpus content that has been prepared for them a priori, and (b) the content is tightly coupled with other parts of the system, which hinders its reusability. This work presents a proposal that leverages the semantic web by extending an existing content provision system, Slice-pedia.

Extract Large and complex datasets collecting digital breadcrumbs of human behaviour have become non-negligible sources for social science researchers, students, and practitioners. ‘Big data’ has made a place for itself as a commodity in academic, commercial, and non-commercial settings. There have been numerous new descriptions for defining social scientists with quantitative expertise: empirical social scientist, computational social scientist, quantitative social scientists, social data scientist, etc. Nonetheless, a social scientist with solid big data skills is not yet a common profile in academia and research institutions. Neither is a computer scientist with broad interdisciplinary skills in social sciences. As the amount of published material increases exponentially in many disciplines, specialisation seems to be a reasonable response to keep up with the state of the art, and to produce technically and conceptually solid research. Interdisciplinary teams are vital in providing the critical synthesis from these separate specialisations, and this book is put together to help bridge the language gap between the different specialisations needed for migration analysis from big data sources.

To meet the grand challenges of agricultural production including climate change impacts on crop production, a tight integration of social science, technology and agriculture experts including farmers are needed. Rapid advances in information and communication technology, precision agriculture and data analytics, are creating a perfect opportunity for the creation of smart connected farms (SCFs) and networked farmers. A network and coordinated farmer network provides unique advantages to farmers to enhance farm production and profitability, while tackling adverse climate events. The aim of this article is to provide a comprehensive overview of the state of the art in SCF including the advances in engineering, computer sciences, data sciences, social sciences and economics including data privacy, sharing and technology adoption. More specifically, we provide a comprehensive review of key components of SCFs and crucial elements necessary for its success. It includes, high-speed connections, sensors for data collection, and edge, fog and cloud computing along with innovative wireless technologies to enable cyber agricultural system. We also cover the topic of adoption of these technologies that involves important considerations around data analysis, privacy, and the sharing of data on platforms. From a social science and economics perspective, we examine the net-benefits and potential barriers to data-sharing within agricultural communities, and the behavioral factors influencing the adoption of SCF technologies. The focus of this review is to cover the state-of-the-art in smart connected farms with sufficient technological infrastructure; however, the information included herein can be utilized in geographies and farming systems that are witnessing digital technologies and want to develop SCF. Overall, taking a holistic view that spans technical, social and economic dimensions is key to understanding the impacts and future trajectory of Smart and Connected Farms.

Research activities are strongly affected by actual developments in computer science, such as in the areas of hardware (increasing processor speed and storage capacity, advances in communication technology), software (powerful tools), information management (data bases, data mining) and media (internet, eLearning, multimedia).

Knowledge transfer in agent-based computational social science

Health and human behavior: areas of interest common to the social and medical sciences.

AI fairness is at the center of many debates, but there are different perspectives on what it entails. Currently, purely technical algorithmic fairness approaches dominate the scene, often neglecting a sufficiently well-rounded view of social implications and ignoring the voices of lay people. With the end goal of overcoming these issues, we investigate and synthesize the points of contact and differences among computer science, sociological and lay people perspectives and move towards a lay-socio-technical view of AI fairness. To this end, we conducted interviews with experts from the computer and social sciences, as well as a survey and co-creation workshops with lay people. Our results show that there are sometimes conflicting views on AI fairness across these perspectives. An integrated view requires two processes of negotiation: (a) between computer and social sciences, and (b) between experts from both disciplines and lay people. We identify strategies for supporting these processes, but we state that they are ultimately possible only if there is a rebalance of current power relations between the disciplines, and between experts and lay people.

IntroductionFollowing the new world order propagated by the US government, economies of the world had taken a new dimension that now adopt the philosophy of Western economists situated in globalisation. Globalisation is a paradigm shiftfrom autarchy to trade liberalisation which encourages nation-states to open their borders from the obstacles and hindrances that hitherto prevented movements of goods, services and peoples into their territories. Thus, globalisation 'summaries a number of international features of the world economy; rapid advances in communication and transportation technology, expanding geographical scope for business activities of private corporation and financial institution, the integration of markets across national borders, and higher degree of uniformity in policy and institutional environments that set the rules of the game for economic actions and interactions on the part of private agents based in various countries (Court and Yamagihara, 1998, cited in Alimi and Atanda, 2011, 345).While this indeed is a paradigm shift, it denied the developing selfdevelopment, self-reliance, self-growth, self-independence and self-absolutism eroding the powers, initiatives, autonomous and autochthonous for growth and development. For decades now globalisation and its branches like privatisation, commercialisation, deregulation and trade liberalisation have become global philosophy and currency that virtually all use to run their economies. No thanks for the fall of Berlin Wall in 1989.Developed economies have imbedded the philosophy of globalisation, the developing are not leftout, from Asia to Africa and to Latin America are scrambling for globalisation since global financial institutions such as World Bank (WB), International Monetary Fund (IMF), and World Trade Organisation (WTO) tied their supportive measures to this currency. Globalisation has encouraged 'international mobility of capital, result from advances in communications technology and liberalisation of financial markets has intensified as the world economy witnesses the unleashing of market forces' (Onwuka and Eguavoen, 2007, 45).Therefore, globalisation encourages the deregulation of domestic markets across the board in the name of privatisation and commercialisation of governmental owned enterprises. The spirit of globalisation is toward trade liberalisation of the world and domestic economies across the board encouraging foreign experts to invest domestically in economies of less developing countries. Thus, globalisation is about bringing: financial capital, investments, experts, technical know-how, technology, markets, services and goods into the less advanced with aim to ensure that these imbibe or accept in totality foreign goods and services, while at the same time discouraging local productions, local experts, local industries and other services as incapable of being produced by the host or peripheral countries.However, the politics of globalisation has two dimensions within one world. The trade debate between the North and South (Adeniran, 1983) produced different views for the one world. Adeniran commented that Bretton Woods system was created to ensure 'monetary stability and economic expansion of the North-the North in this context implying the industrially more developed areas of the world, to the disadvantage of the South which comprises of the nations from the Third World' (1983, 154). Thus, globalisation has continued to create asymmetrical relationship between the North and South. For instance, the North as a result of advancement and quantum resources dictated economic and developmental strategies for the South. Such strategies included: good governance, democracy, accountability, transparency, Structural Adjustment Programme which had severe consequences on some in the South. According to Blake and Walters 'the benefits of such international economic relations between rich and poor states are distributed asymmetrically in favour of the rich. …

Addressing the policy challenges and opportunities of “Big data”

Objectives Although Artificial Intelligence (AI) has been a part of the computer science field for many decades, it has only recently been applied to different areas of behavioral and social sciences. This article provides an examination of the applications of AI methodologies to behavioral and social sciences exploring the areas where they are now utilized, the different tools used and their effectiveness. Methods The study is a systematic research examination of peer-reviewed articles (2010–2019) that used AI methodologies in social and behavioral sciences with a focus on children and families. Results The results indicate that artificial intelligence methodologies have been successfully applied to three main areas of behavioral and social sciences, namely (1) to increase the effectiveness of diagnosis and prediction of different conditions, (2) to increase understanding of human development and functioning, and (3) to increase the effectiveness of data management in different social and human services. Random forests, neural networks, and elastic net are among the most frequent AI methods used for prediction, supplementing traditional approaches, while natural language processing and robotics continue to increase their role in understanding human functioning and improve social services. Conclusions Applications of AI methodologies to behavioral and social sciences provide opportunities and challenges that need to be considered. Recommendations for future research are also included.KeywordsArtificial intelligenceBehavioral and social sciencesMachine learningFamiliesChildren

Social scientists and computer scientist are divided by small differences in perspective and not by any significant disciplinary divide. In the field of text analysis, several such differences are noted: social scientists often use unsupervised models to explore corpora, whereas many computer scientists employ supervised models to train data; social scientists hold to more conventional causal notions than do most computer scientists, and often favor intense exploitation of existing algorithms, whereas computer scientists focus more on developing new models; and computer scientists tend to trust human judgment more than social scientists do. These differences have implications that potentially can improve the practice of social science.

With the increasing adoption of intelligent tutoring systems, accurately interpreting students’ emotional states in educational contexts is crucial for providing personalized learning support. In computer science, natural language processing (NLP) techniques offer promising solutions for sentiment analysis and academic performance prediction. In the field of Chinese language education, students’ emotional states significantly influence their learning outcomes. However, traditional sentiment analysis methods exhibit limited adaptability to educational texts, failing to capture multi-granularity emotional expressions effectively. To bridge this gap, this study proposes a Transformer-based multi-granularity sentiment analysis framework tailored specifically for Chinese educational texts, integrating sentiment classification with learning outcome prediction. Our approach operates across three distinct levels, sentence, paragraph, and full text, to extract nuanced emotional features comprehensively. Furthermore, we develop a predictive model that integrates these sentiment features with learning behavior data to estimate students’ academic performance accurately. Experimental results demonstrate that our framework consistently outperforms traditional and deep learning baseline models in sentiment classification and learning outcome prediction tasks. These findings highlight the substantial potential of NLP techniques to enhance adaptive learning strategies and optimize personalized learning experiences.

Synthetically analyze the power distribution communication network technology in the smart power grid link. It mainly includes the summary of the smart distribution network, electric power communication backbone network and then layer communication requirements, the network mode and the application mode of WLAN technology in the distribution network, EPON network technology, micropower wireless technology, PTN technology and power broadband wireless systems. State the demand of the smart power distribution business and put forward technical guidance for the smart power distribution communication network scheme. In view of the complex distribution network architecture, more nodes and scattered, complex business types data business needs, put forward the integrated solutions of optical fiber communication as the backbone access, wireless bandwidth, passive optical communication as the coverage, the short distance wireless communication as the main terminal access. It provides a reference for the communication plan of distribution side.