Table of Contents
Overview
Definition and Scope
Computational social science is an interdisciplinary academic sub-field that combines computational methods with social science to analyze and model social phenomena. This field utilizes computers to simulate and analyze various aspects of human behavior and societal trends, drawing on large-scale data sources such as social media interactions and digital footprints.[2.1] By leveraging advanced data analysis, mathematical algorithms, and computational modeling, researchers gain profound insights into human activity and relationships.[4.1] The emergence of computational social science has been significantly influenced by the growing availability of social and behavioral data, particularly due to the rise of the Internet and social media over the past decade. This explosion of data presents unprecedented opportunities for social scientists to study human behavior and social interactions in greater detail than ever before.[5.1] The field not only encompasses traditional social science disciplines but also incorporates techniques from computer science and statistics, thereby enhancing the analytical capabilities available to researchers.[4.1] Computational social science is increasingly recognized for its critical role in analyzing and addressing complex societal issues, particularly misinformation and polarization. The massive and often systematic spread of inaccurate information on the internet and social media poses significant risks to society, fueled by various cognitive, social, and algorithmic biases. Traditional countermeasures, such as journalistic corrections, have proven inadequate in scaling effectively to combat this digital misinformation.[13.1] To enhance real-time detection of misinformation, researchers have proposed innovative frameworks like the Cyclic Evidence-based Misinformation Detection (CEMD), which addresses challenges such as label redundancy and cold start by incorporating mechanisms that access the latest external knowledge.[14.1] Furthermore, understanding the dynamic mechanisms of misinformation creation and dissemination in social networks is essential, as it involves analyzing cognitive vulnerabilities that hinder the correction of ingrained inaccurate narratives.[15.1] The advent of information distribution mechanisms, characterized by self-exploration and algorithmic influences, has raised concerns about the reinforcement of misinformation beliefs and the resulting polarization.[16.1] Thus, an interdisciplinary approach is vital for developing effective strategies to mitigate the adverse effects of misinformation on society.Key Components
Computational Social Science (CSS) integrates diverse methodologies and technologies to analyze social phenomena, particularly through social media and big data. A key component of CSS is the use of social media data, which is crucial for understanding public opinion and addressing challenges like misinformation and polarization. The vast amounts of accessible data from social media platforms have sparked a "data gold rush," attracting both social scientists and corporate entities.[6.1] This convergence necessitates computational approaches to analyze the extensive volumes of communication data available, prompting a rethinking of interpersonal and mass communication.[7.1] The impact of social media algorithms on public discourse is another critical aspect of CSS. Since Facebook's News Feed was introduced in 2006, concerns have emerged about algorithmic manipulation and misinformation, which significantly shape online experiences and public perception.[8.1] Research highlights how these algorithms amplify misinformation, contributing to political polarization and eroding trust in institutions.[9.1] The global spread of misinformation, facilitated by these algorithms, poses challenges to democratic governance and societal cohesion, as false information can quickly cross national borders.[10.1] CSS has also evolved to include network analysis as a fundamental pillar, offering insights into the complexities of social networks. This approach is essential for addressing issues like misinformation and polarization.[12.1] Ethical considerations in data collection and analysis are paramount, requiring researchers to navigate challenges related to data provenance, privacy, and data usage.[19.1] Strategies to ensure data quality and minimize bias include combining qualitative and quantitative methods, enhancing the reliability of findings.[18.1] The methodologies in CSS are being refined to tackle the spread of misinformation on the Web and social media. This challenge is exacerbated by cognitive, social, and algorithmic biases that allow misinformation to thrive. The integration of computational techniques with social sciences offers a powerful opportunity to understand the causes and consequences of misinformation, providing hope for improving the information environment.[20.1] By employing methods to measure and combat misinformation, researchers can develop effective strategies to mitigate its impact on public opinion and societal discourse, moving beyond traditional countermeasures to create a robust framework for addressing misinformation complexities in digital spaces.[20.1]In this section:
History
Early Developments
The origins of computational social science can be traced back to the late 1950s and early 1960s, a period marked by the increasing adoption of mainframe computers in government and industry. During this time, political and social scientists began to explore innovative ways to leverage this emerging technology for their research purposes.[43.1] This integration of social science with computer science and engineering led to the establishment of computational social science as a distinct area of study, which applies computational methods to analyze novel sources of digital data, including social media, administrative records, and historical archives.[44.1] The formal recognition of computational social science as a discipline occurred in February 2009, when a group of 15 researchers published a seminal paper in the journal Science, announcing the emergence of the field. Among the coauthors was computer scientist Alex Pentland from the Massachusetts Institute of Technology, who acknowledged that declaring the birth of a new field was somewhat premature, yet indicative of the growing interest and potential within this area.[45.1] Since its inception, computational social science has rapidly evolved, with a significant increase in the volume of research output. Over the past decade, thousands of papers have been published that utilize observational data, experimental designs, and large-scale simulations, which were previously unfeasible for researchers. This surge in scholarly activity has greatly enhanced our understanding of various social phenomena, including social inequality and the dynamics of information spread.[47.1] The field has also found applications in diverse areas such as computational economics, computational sociology, and culturomics, among others, employing data science techniques like machine learning and network analysis to investigate social and behavioral relationships.[46.1]Evolution of Methodologies
The evolution of methodologies in computational social science (CSS) has been profoundly shaped by technological advancements and data analysis techniques. Initially, sociologists employed traditional methods to study social networks, but the rise of big data and sophisticated analytical techniques has elevated the prominence of social network analysis (SNA) within CSS.[56.1] The advent of big data has been pivotal in the emergence of CSS, which seeks to elucidate social processes through computational models, thereby transforming the landscape of social science research.[54.1] As CSS has progressed, it has increasingly harnessed mathematical algorithms, advanced data analysis, and computational modeling to study and predict human behavior and social dynamics.[53.1] This interdisciplinary approach enables researchers to decode complex social phenomena, revealing insights that were previously inaccessible through traditional methods.[55.1] The ongoing digital revolution, marked by advancements in artificial intelligence (AI), machine learning (ML), and data science, presents both opportunities and challenges for studying social life.[58.1] This shift underscores the importance of utilizing these new tools to understand how technologies shape social dynamics and necessitates a reevaluation of existing social science research.[58.1] Engaging with AI, ML, and data science not only opens new research avenues but also highlights the risks and challenges associated with these technologies.[58.1] The evolution of methodologies in CSS has also been significantly influenced by data-driven approaches, particularly in the study of human dynamics, poverty, wealth, and epidemics. This shift reflects a growing interest in data-centered research topics, as evidenced by the first comprehensive survey of application domains involving human dynamics in CSS.[57.1] The digital revolution has necessitated a reevaluation of traditional social science research methods, opening new avenues for understanding complex social phenomena.[58.1] However, integrating large digital datasets and computational methods in CSS raises critical ethical concerns, including issues of data privacy, informed consent, and potential data misuse.[48.1] These ethical challenges are compounded by the predictive capabilities of big data, which can expose participants' vulnerabilities and complicate adherence to ethical principles in research.[49.1] As CSS continues to evolve, it is crucial to navigate these complexities while leveraging the opportunities presented by new technologies.In this section:
Recent Advancements
Integration of Big Data
The integration of Big Data Analytics (BDA) into computational social science has significantly transformed the prediction of consumer behavior. By leveraging a variety of technologies, including machine learning and natural language processing, BDA enables the analysis of large and complex datasets to uncover patterns and predict future consumer actions.[113.1] This analytical capability acts as a powerful tool for businesses, allowing them to personalize offerings and enhance customer satisfaction through data-driven insights.[115.1] Recent studies have highlighted the effectiveness of various machine learning algorithms, such as Decision Trees, Random Forests, and Logistic Regression, in predicting customer behavior. These algorithms have demonstrated superior performance metrics, including high precision and recall rates, which are crucial for accurately anticipating consumer trends and preferences.[116.1] The application of predictive modeling principles in BDA not only aids in identifying consumer patterns but also provides valuable insights that can inform retail strategies.[114.1] The integration of Big Data in consumer behavior analysis raises significant ethical challenges that warrant careful consideration. Key ethical concerns include privacy issues related to the collection and use of personal data, the necessity for accuracy and transparency in data handling, and the potential for bias and unfairness in algorithmic decision-making.[117.1] Furthermore, the Revised Common Rule allows for broad consent in research involving publicly available information, which can lead to situations where participants are not fully informed about how their data is utilized. This lack of informed consent is particularly troubling, as it undermines respect for participants' autonomy and privacy.[119.1] As the field of Big Data analytics continues to evolve, it is essential to address these ethical considerations to ensure responsible practices in understanding consumer behavior.[118.1]Advances in Computational Techniques
Recent advancements in computational techniques have significantly enhanced the field of Computational Social Science (CSS), enabling researchers to analyze complex social phenomena with unprecedented depth and accuracy. The integration of computer science, statistics, and social sciences has led to the development of innovative methodologies that leverage large-scale demographic, behavioral, and network data to investigate human activity and relationships.[85.1] This interdisciplinary approach allows for the application of algorithms, simulations, and data analytics, which can uncover patterns that traditional social science methods might overlook.[86.1] One of the most notable advancements is the use of big data analytics, which has transformed how researchers decode social phenomena. By utilizing novel sources of digital data, such as social media and administrative records, CSS has developed theories of human behavior that were previously inaccessible.[90.1] This shift towards a data-driven paradigm has opened new avenues for understanding social dynamics, particularly in areas such as consumer behavior, where the interaction with social environments significantly influences purchasing and consumption patterns.[91.1] Moreover, the advent of generative AI techniques, including Generative Adversarial Networks (GANs) and transformers, has revolutionized consumer behavior prediction. These models excel at processing complex sequential data and generating realistic datasets, thereby enhancing the accuracy of predictions related to customer behaviors, such as churn and purchasing intent.[93.1] The application of these advanced algorithms not only improves theoretical insights but also has practical implications in marketing and customer engagement.[93.1]In this section:
Methodological Approaches
Data-Driven Approaches
Data-driven approaches in computational social science leverage extensive datasets and advanced analytical techniques to explore social phenomena. One of the primary methodologies employed is network analysis, which examines the relationships and interactions among individuals or groups within social networks. This approach allows researchers to uncover patterns of connectivity and information flow that traditional methods may overlook, thereby enhancing the understanding of social dynamics.[130.1] In addition to network analysis, computational social science utilizes various data science techniques, including machine learning and rule-based analysis, to investigate behavioral relationships and interactions.[130.1] These methodologies enable researchers to model complex social behaviors and predict outcomes in diverse contexts such as economics and politics.[128.1] Ensuring high data quality is crucial in computational social science, as it directly influences the reliability of research findings. The literature outlines a variety of techniques to assess and improve data quality, highlighting the importance of careful data management and processing, which includes data validation and cleaning methods that address common issues such as missing values and mislabels.[132.1] While traditional frameworks for evaluating data quality remain relevant, they require updates to adapt to the new challenges presented by big data across various fields, including computational social science.[133.1] Recent research has increasingly focused on developing methodologies that aid in the selection, customization, and application of these data quality assessment techniques, reflecting the diversity and complexity of the challenges involved.[135.1] The integration of qualitative methods with quantitative data analysis is increasingly recognized as beneficial in social science research. Qualitative research can expand and deepen the understanding of data obtained from quantitative analysis, providing insights that help contextualize and interpret the numerical findings more effectively.[142.1] While quantitative research typically prescribes a controlled environment for data collection, qualitative methods may involve gathering data in a central location or within the participants' natural environments, depending on the study's goals.[142.1] Techniques such as structured and unstructured interviews, focus groups, and participant observation not only assist in generating hypotheses that can be rigorously tested through quantitative research but also enable researchers to delve deeper into the meaning of quantitative results and their implications.[142.1] As triangulation of methods has become a significant strategy in contemporary social science research, understanding the advantages and limitations of mixing these methods is essential for enhancing the overall research process.[141.1]Model-Driven Approaches
Model-driven approaches in computational social science aim to reduce representation, sampling, and measurement errors associated with digital data.[154.1] These methodologies often involve substituting traditional data collection methods, such as web surveys, with digital data sources, which can include measuring opinions through digital traces.[154.1] Additionally, these approaches extend beyond merely tracking or donating data from applications, search terms, and URLs, allowing for a more comprehensive understanding of social phenomena.[154.1] By effectively integrating these methodologies, researchers can enhance the overall quality of their analyses and contribute to the development of more robust computational models.[154.1] The era of big data has significantly transformed the landscape of social science research, presenting new opportunities for researchers to utilize micro-level, meso-level, and macro-level data. This shift suggests a potential scientific paradigm change towards computational social science, where traditional data collection methods are increasingly supplemented or replaced by digital data sources, such as web surveys and digital traces of user behavior.[153.1] Computational social science employs algorithms, simulations, and data analytics to uncover patterns that may remain hidden when using conventional social science methods. This interdisciplinary field focuses on key areas such as network analysis, which examines the connections between individuals or groups and the flow of information through social networks, and behavioral modeling, which predicts or simulates human behavior across various contexts, including economics, politics, and online communities.[155.1] Furthermore, the integration of traditional social science theories and methodologies into computational models is essential for bridging the gap between these two approaches. Researchers are encouraged to consider how established social science theories can inform the design of computational models, ensuring that the insights gained from computational analyses are grounded in robust theoretical frameworks.[152.1] This synthesis not only enhances the validity of computational models but also enriches the understanding of complex social processes.Applications
Social Media Analysis
Computational social science plays a significant role in analyzing social media, particularly in understanding political communication and the dynamics of misinformation. The advent of large text datasets has transformed traditional qualitative methods, such as thematic and content analysis, by enabling the use of automated techniques like natural language processing (NLP). These computational methods outperform traditional analyses by efficiently processing vast amounts of text data, thus providing deeper insights into social phenomena.[180.1] In the realm of political campaigns, computational social science methodologies are increasingly utilized to identify and counteract misinformation. This approach employs a mixed-methods strategy that integrates quantitative analysis of data from various social media platforms with qualitative research, including interviews with individuals who design and implement political bots and disinformation campaigns.[193.1] By leveraging these methodologies, researchers aim to address critical questions regarding the impact of misinformation on public opinion and the overall quality of democracy in the digital information era, particularly in relation to online hate and foreign influence campaigns.[191.1] The rise of social media has significantly complicated the challenge of disentangling political communication processes and their effects within the new political information environment.[173.1] Computational politics, which merges computer science with political science, employs various computational methods, including analysis tools and prediction techniques, to address questions pertinent to political behavior and communication.[172.1] This intersection is particularly relevant in the context of political campaigns, where concerns about misinformation, online hate, and foreign influence campaigns have emerged as critical threats to the quality of democracy.[192.1] By utilizing computational social science methodologies, researchers can effectively measure public opinion and counteract these pernicious forms of online behavior, thereby enhancing the integrity of democratic processes in the digital age.[192.1] To effectively identify and counter electoral misinformation, a variety of tools and techniques have been developed within the field of computational social science. This toolkit includes practical resources such as fact-checking tools, case studies on real-world misinformation campaigns, and strategies aimed at promoting credible information.[194.1] These resources are designed to empower individuals with the knowledge and actionable steps necessary to foster a more informed public.[194.1] Furthermore, computational social scientists are actively exploring methodologies to address concerns related to misinformation, online hate, and foreign influence campaigns, which have emerged as significant issues in the digital information era.[191.1] By focusing on these areas, computational social science plays a crucial role in enhancing the quality of democracy in the context of social media.[191.1]In this section:
Interdisciplinary Connections
Collaboration with Computer Science
Computational social science (CSS) represents a significant interdisciplinary collaboration between social sciences and computer science, leveraging computational methods to analyze and model complex social phenomena. This interdisciplinary approach is crucial as it allows researchers to apply algorithms, simulations, and data analytics to uncover patterns that traditional social science methods may overlook, thereby facilitating a more nuanced understanding of social processes on a large scale.[206.1] Key areas of focus within CSS include network analysis and behavioral modeling. Network analysis examines how individuals or groups are interconnected and how information disseminates through social networks, while behavioral modeling predicts or simulates human behavior across various contexts, such as economics, politics, and online communities.[206.1] The integration of computational tools with traditional social science methodologies enhances the ability to address complex social phenomena, moving beyond mere descriptions and predictions to provide deeper explanations of social dynamics.[221.1] The rise of big data has further propelled the relevance of CSS, enabling researchers to conduct studies using observational data, experimental designs, and large-scale simulations that were previously unfeasible.[222.1] This shift has not only expanded the methodological toolkit available to social scientists but has also fostered a scientific paradigm shift towards computational approaches in social research.[223.1] The collaboration between disciplines is exemplified in areas such as computational diplomacy, where a multi-layered network approach is employed to improve understanding of institutional complexity and governance design.[209.1] Moreover, fostering interdisciplinary collaboration is essential for enhancing the effectiveness of research in CSS. The presence of "interpreters"—individuals capable of bridging various scientific domains—plays a vital role in facilitating communication and collaboration among researchers from different fields.[210.1] As CSS continues to evolve, the integration of qualitative research methods with computational techniques is also gaining attention, highlighting the potential for a hybrid approach that enriches data interpretation and analysis.[217.1]Influence on Sociology and Other Fields
The integration of computational methods into social sciences has significantly influenced the field of sociology and other related disciplines. This interdisciplinary approach has led to the emergence of computational social science (CSS), which combines tools and techniques from computer science with theories and methods from social sciences, thereby expanding the understanding of human behavior at scale.[236.1] One of the key impacts of CSS is its ability to analyze vast amounts of digital data, such as social media interactions, administrative records, and historical archives. This capability allows researchers to develop more robust theories of human behavior, moving beyond traditional qualitative insights to include quantitative data.[235.1] The fusion of these methodologies offers enormous possibilities for enhancing sociological research, enabling scholars to explore complex social dynamics that were previously difficult to quantify.[236.1] The integration of computational methods into sociology signifies a notable departure from traditional social theory, which is typically articulated in natural language and employs a rich conceptual vocabulary. This traditional approach allows for nuanced discussions but also introduces ambiguity; a comprehensive theory can accommodate various outcomes, which may lead to explanations that lack specificity.[237.1] In contrast, computational sociologists often express their theories using mathematical and algorithmic language, which can enhance clarity and precision in understanding social phenomena.[237.1] However, the shift towards computational methods does not inherently resolve the complexities involved in merging qualitative insights with quantitative data, as the interdisciplinary nature of this approach presents challenges that require careful consideration of both qualitative richness and quantitative rigor.[237.1] The implications of computational social science (CSS) are significant across various fields, particularly in public health, as advancements in network analysis techniques and technological innovations in communication have created opportunities for new theoretical perspectives. These developments emphasize the importance of social and environmental behavioral influences, thereby broadening the application of social networks to public health topics.[214.1] Within social networks, relationships can be categorized into strong ties and weak ties. Strong ties are characterized by close relationships and frequent interactions, such as those between family members and close friends, while weak ties emerge from more distant social relationships with infrequent interactions.[227.1] Research indicates that although strong ties play an important role in information dissemination, weak ties are often more effective in spreading information due to their connections to diverse social circles and access to heterogeneous resources.[229.1] This distinction is crucial, as the strength of ties influences the speed and spread of crisis information dissemination, with weak ties facilitating more rapid information diffusion.[227.1] Understanding these dynamics is essential for enhancing the effectiveness of communication strategies in public health initiatives and crisis management.[214.1]Challenges And Limitations
Methodological Rigor
Methodological rigor in computational social science (CSS) is crucial for ensuring research findings are both valid and reliable. The unique nature of social network data, often non-Euclidean, presents a significant challenge, complicating the application of traditional deep learning technologies and necessitating the creation of new methodologies tailored to these data structures.[249.1] The vast digital traces generated in modern society introduce conceptual, computational, and ethical challenges, requiring a rejuvenation of scientific theories to align with evolving social realities and computational capabilities.[261.1] Addressing these complexities is vital for maintaining the validity of CSS research findings.[261.1] The integration of computational and social sciences demands a blend of explanatory and predictive approaches, known as 'integrative modelling'.[267.1] This process involves reconciling different paradigms to create a cohesive framework capable of addressing complex social phenomena.[267.1] Furthermore, the reliance on survey data, often structured for variable-based analysis, underscores the need for innovative data collection methods that reflect the dynamic nature of social interactions.[265.1] To enhance methodological rigor, it is essential to develop dedicated research infrastructures that promote collaboration between social scientists and experts from fields such as computer science, physics, and mathematics.[254.1] These interdisciplinary efforts are crucial for advancing CSS beyond traditional qualitative studies and realizing larger-scale projects.In this section:
Future Directions
Emerging Trends
Emerging trends in computational social science (CSS) reflect the field's rapid evolution and the integration of advanced computational techniques with social science methodologies. One significant trend is the increasing availability of large-scale social and behavioral data, which has expanded the scope of research and analysis in CSS. This surge in data availability has been accompanied by advancements in computing power, enabling researchers to engage with complex social phenomena at unprecedented scales.[293.1] The interdisciplinary nature of CSS is another emerging trend, as it combines social science theories with computational methods to analyze and model social phenomena. This fusion allows for the application of algorithms, simulations, and data analytics to uncover patterns that traditional social science methods may overlook. Key areas of focus within this field include network analysis, which examines the connections between individuals or groups and the flow of information through social networks, and behavioral modeling, which predicts or simulates human behavior across various contexts such as economics, politics, and online communities.[294.1] Furthermore, the integration of big data analytics into CSS is transforming the way researchers decode complex social phenomena. This approach not only enhances the understanding of social processes but also emphasizes the practical applications of CSS insights in real-world scenarios. The use of artificial intelligence and machine learning is particularly noteworthy, as these technologies provide tools capable of processing and interpreting vast amounts of data in ways that exceed human capabilities. This trend underscores the potential of CSS to contribute to a more equitable framework, ensuring that the benefits of data-driven insights are accessible to diverse segments of society.[295.1]Potential Impact on Social Sciences
The integration of machine learning (ML) techniques into social sciences is poised to significantly enhance the methodologies employed in computational social science. One of the primary challenges in this integration is the ability of ML models to capture nonlinear relationships and generalize to new, unseen data, which is crucial for effective social science research.[297.1] Moreover, the interpretability of complex models, such as neural networks and ensemble methods, remains a significant hurdle, as these models are often perceived as "black boxes" due to their intricate decision-making processes.[297.1] Social network analysis (SNA) exemplifies the transformative potential of ML in social sciences. By encoding network data into low-dimensional representations, researchers can facilitate various applications such as classification, link prediction, anomaly detection, and clustering.[296.1] The symbiotic relationship between SNA and ML is essential for recognizing and decoding complex patterns within social networks, thereby addressing key research questions and enhancing analytical capabilities.[299.1] Advanced methodologies, including reinforcement learning and graph neural networks (GNNs), are being leveraged to tackle the inherent complexities of social networks.[300.1] The rapid growth of social networks has also introduced challenges related to data gathering techniques, heterogeneity, scalability, and missing data.[298.1] These challenges necessitate innovative approaches to data collection and analysis, which can be addressed through collaborative efforts between social scientists and computational experts.[301.1] The field of computational social science thrives on this collaboration, as social scientists provide vital context and insights that inform the development of novel computational methods.[301.1] The integration of traditional social science theories with computational methods is becoming increasingly significant as the field of computational social science evolves. This integration is exemplified by the shift towards open science practices, which encourages social scientists to adopt methods that reduce analytical flexibility, while computer scientists employ crowdsourcing techniques, such as the "common task framework," to achieve larger modeling objectives.[307.1] Theoretical models and approaches in computational social science often arise from established social science theories, demonstrating pathways between the two fields.[306.1] However, there are concerns that computational social science may challenge traditional theories, as it emphasizes relevant rules and mechanisms that may not align with conventional theoretical frameworks.[308.1] As researchers continue to explore how to effectively combine causality and prediction within computational social sciences, the potential for new insights into social dynamics remains substantial.[308.1]In this section:
References
[2] Computational Social Science - Penn Computer & Information Science ... — Computational Social Science is an interdisciplinary research area that combines social science with computational methods to analyze and model social phenomena. It leverages large-scale data, such as social media interactions, digital footprints, and online behaviors, to gain insights into human behavior, societal trends, and group dynamics.
[4] Computational Social Science - The Decision Lab — Computational social science is an interdisciplinary field that leverages mathematical algorithms, advanced data analysis, and computational modeling to study and predict human behavior and social dynamics. By integrating techniques from computer science, statistics, and social sciences, computational social science offers powerful insights
[5] Computational Social Science - Cornell Information Science — Growing use of the Internet and social media in the past decade has led to an explosion in the amount of social and behavioral data available to researchers. This in turn has created huge opportunities for social scientists to study human behavior and social interaction in unprecedented detail. Leveraging these opportunities requires collaborative, interdisciplinary efforts involving computer
[6] Social media and the social sciences: How researchers employ Big Data ... — Social media have rendered the opinions and interactions among complex networks of individuals accessible and searchable. Such data is of interest to social scientists as well as government and corporate interests. Many researchers recognize a computational turn, some calling it a "data gold rush" (Kennedy et al., 2014). The phrase gold rush is associated with the growing commodification
[7] Vectors into the Future of Mass and Interpersonal Communication ... — The convergence of big data, social media, and computational social and communication science allow researchers to rethink interpersonal and mass communication. The volumes of communication data at our disposal -- and readily harvested -- require computational approaches to their understanding (Shah, Cappella, & Neuman, 2015), provide access to public opinion that is unique and unprecedented
[8] The Misconceptions of Public Discourse Regarding Online Misinformation ... — The narrative surrounding social media's impact on society is often dominated by claims of algorithmic manipulation and the rampant spread of misinformation. Since the introduction of Facebook's News Feed in 2006, public discourse has focused on the power of these algorithms to shape our online experiences, culminating in recent concerns
[9] Student Research Guide WQ25/ Impact of Social Media Algorithms on ... — The research thesis of this guide is to analyze the role of social media algorithms in amplifying misinformation and shaping public perception. By examining the mechanisms behind algorithmic content selection, this guide explores how these systems contribute to political polarization, erode trust in institutions, and impact public discourse.
[10] Advancing the Study of Political Misinformation Across Countries and ... — The global proliferation of misinformation poses profound challenges to democratic governance, public discourse, and societal cohesion. In the digital age, the rapid dissemination of false or misleading information has been amplified by the algorithms and affordances of social media platforms, allowing content to transcend national borders with unprecedented speed.
[12] Networks as a Path to Distinction (Computational Social Science 1) — This article is divided into two parts, each highlighting a core methodological domain within Computational Social Science(CSS). Part I (you are now reading) explores network analysis as a fundamental pillar of CSS, detailing how relational data and complex network structures have become central to understanding social systems.Part II will shift focus to social simulation, examining how agent
[13] Fighting fake news: a role for computational social science in the ... — The massive, uncontrolled, and oftentimes systematic spread of inaccurate and misleading information on the Web and social media poses a major risk to society. Digital misinformation thrives on an assortment of cognitive, social, and algorithmic biases and current countermeasures based on journalistic corrections do not seem to scale up. By their very nature, computational social scientists
[14] GitHub - Computational-social-science/CEMD: Real-Time Misinformation ... — Prior real-time misinformation detection tasks have been hindered by the dual problems of label redundancy and cold start. To this end, we propose a novel Cyclic Evidence-based Misinformation Detection (CEMD) framework, which incorporates two core mechanisms: (i) a Retrieval Augmented Generation (RAG) pipeline that accesses the latest external knowledge to augment insufficient prior knowledge
[15] Digital media and misinformation: An outlook on multidisciplinary ... — This review discusses the dynamic mechanisms of misinformation creation and spreading used in social networks. It includes: (1) a conceptualization of misinformation and related terms, such as rumors and disinformation; (2) an analysis of the cognitive vulnerabilities that hinder the correction of the effects of an inaccurate narrative already assimilated; and (3) an interdisciplinary
[16] How Misinformation Diffuses on Online Social Networks: Radical Opinions ... — The advent of information distribution mechanism constituted by self-exploration, network neighbors, and especially algorithms, has aroused widespread concerns about the reinforcement of misinformation beliefs and the resulting polarization. However, few existing researches fully consider the inherent characteristics of misinformation (e.g. evoking repulsive effects), as well as the adaptive
[18] Challenges of Data Collection in Social Research — Challenges of Data Collection in Social Research • Sociology Notes by Sociology.Institute Challenges of Data Collection in Social Research Understanding the Data Collection Process in Social Research Understanding the Data Collection Process in Social Research 🔗 Despite the inherent challenges in data collection, there are several strategies researchers can use to minimize bias and navigate ethical dilemmas effectively. By combining qualitative and quantitative methods or using different types of data collection instruments (like surveys and interviews), researchers can cross-check their findings and reduce the likelihood of bias. Have you encountered any challenges in data collection, whether as a researcher or as a participant?
[19] The Ethics of Managing People's Data - Harvard Business Review — The Ethics of Managing People’s Data Business and society|The Ethics of Managing People’s Data The Ethics of Managing People’s Data According to the authors, managers who are examining projects that involve gathering human-provided data or leveraging existing databases need to focus on five critical issues: the provenance of the data, the purpose for which it will be used, how it is to be protected, how the privacy of the data providers can be ensured, and how the data is prepared for use. Read more on Business and society or related topics Business ethics and Information management A version of this article appeared in the July–August 2023 issue of Harvard Business Review. Read more on Business and society or related topics Business ethics and Information management HBR Store HBR 20-Minute Managers About HBR Manage My Account HBR Store HBR 20-Minute Managers About HBR Manage My Account
[20] Editorial: Multidisciplinary Approaches to Mis- and Disinformation ... — The combination of computational approaches offering methods and techniques to measure and combat the problem with social scientific disciplines that seek to understand causes and consequences is, potentially, particularly powerful, and offers genuine hope that the information environment might be "cleaned up" as it appears it has been
[43] Computational Social Science - Oxford Bibliographies — "Computational Social Science" published on by null. Origins. Early on in the development of computing, with the growing adoption of mainframe computers in government and industry in the late 1950s and early 1960s, political and social scientists began to develop innovative ways to take advantage of this new technology.
[44] Computational Social Science and Sociology | Annual Reviews — The integration of social science with computer science and engineering fields has produced a new area of study: computational social science. This field applies computational methods to novel sources of digital data such as social media, administrative records, and historical archives to develop theories of human behavior. We review the evolution of this field within sociology via
[45] Core Concepts: Computational social science - PMC - National Center for ... — Society in High Resolution. Although it builds on traditional methods, computational social science is a young discipline. In February 2009, 15 researchers published a paper in Science announcing the emergence of the field ().Computer scientist Alex Pentland of the Massachusetts Institute of Technology, one of the paper's coauthors, admits that declaring the birth of a new field was "a bit
[46] Computational social science - Wikipedia — Computational social science - Wikipedia Computational social science It has been applied in areas such as computational economics, computational sociology, computational media analysis, cliodynamics, culturomics, nonprofit studies. It focuses on investigating social and behavioral relationships and interactions using data science approaches (such as machine learning or rule-based analysis), network analysis, social simulation and studies using interactive systems. Computational social science articles are published across several journals, such as New Media & Society, Social Science Computer Review, PNAS, Political Communication, EPJ Data Science, PLOS One, Sociological Methods & Research and Science. Journal of Computational Social Science "Computational social science". Computational social science. Computational Social Science and Sociology. Wikimedia Commons has media related to Computational social science. Retrieved from "https://en.wikipedia.org/w/index.php?title=Computational_social_science&oldid=1277997148" Computational social science Computational social science
[47] Computational social science: Obstacles and opportunities — The field of computational social science (CSS) has exploded in prominence over the past decade, with thousands of papers published using observational data, experimental designs, and large-scale simulations that were once unfeasible or unavailable to researchers. These studies have greatly improved our understanding of important phenomena, ranging from social inequality to the spread of
[48] A practitioner-centered policy roadmap for ethical computational social ... — ABSTRACT. Background: Computational Social Science (CSS) utilizes large digital datasets and computational methods to study human behavior, raising ethical concerns about data privacy, informed consent, and potential misuse. Methods: This study employs a constructivist grounded theory approach, analyzing 15 in-depth interviews with CSS practitioners in Germany, Austria, and Switzerland.
[49] Ethical Issues in Social Science Research Employing Big Data — Ethical Issues in Social Science Research Employing Big Data - PMC Ethical Issues in Social Science Research Employing Big Data This paper explores ethical issues of employing big data1 in social science research (SSR) with a specific focus on how these practices challenge the integrity and ethics of research. In cases where SSR exposes participants’ personal characteristics and vulnerabilities (Nissenbaum & Patterson, 2016), using big data sets might enable researchers to predict participants’ future behavior (and behavioral patterns), which complicates upholding principles of respect for subjects and social responsibility.14 When predictive research efforts are coupled with commercial interests, they have resulted in unfair exclusion of vulnerable groups from opportunities (e.g., access to credit) or led to predatory marketing campaigns (Madden et al., 2017).
[53] Computational Social Science - The Decision Lab — Computational social science is an interdisciplinary field that leverages mathematical algorithms, advanced data analysis, and computational modeling to study and predict human behavior and social dynamics. ... As technology continues to evolve through advancements in machine learning and AI,
[54] Computational Social Science, Big Data, and Networks — The emergence of Big data and a quantified social space has prompted the birth of a new science, computational social science (CSS), whose roots are founded in research aiming to describe social processes using computational models. Big data now fuels rapid advancements in the field, providing the basis for building models and algorithms of
[55] Computational Social Science: Big Data Analytics for Societal Insights ... — Computational Social Science: Big Data Analytics for Societal Insights | Graph AI Computational Social Science: Big Data Analytics for Societal Insights Computational Social Science (CSS) leverages big data analytics to decode complex social phenomena, enabling researchers to unearth insights that were previously inaccessible. This symbiotic relationship between data analysis and practical application highlights the transformative potential of Computational Social Science in both research and real-world scenarios. This holistic approach is vital in building a more equitable framework for computational social science, where the benefits of data-driven insights are accessible to all segments of society. Artificial intelligence and machine learning are set to revolutionize Computational Social Science by providing tools that can process, analyze, and interpret data in ways that humans alone cannot.
[56] Big data, computational social science, and other recent innovations in ... — While sociologists have studied social networks for about one hundred years, recent developments in data, technology, and methods of analysis provide opportunities for social network analysis (SNA) to play a prominent role in the new research world of big data and computational social science (CSS).
[57] Data-Driven Computational Social Science: A Survey — In this paper, to the best of our knowledge, we present a survey on data-driven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. Therefore, exploring the data-centered research topics in computational social science area has attracted more and more attentions. To the best of our knowledge, this study is the first to survey the application domains involving human dynamics in the data-driven computational social science area. Notable examples of artificial societies include SugarScape,30 the artificial stock market,31 and National Planning Scenario 1 (NPS1).32 Besides social simulation, data mining methods focus on discovering knowledge about human dynamics,33 poverty and wealth,34 epidemics,35 and other facets of social systems from large social datasets, offering new insights into social system analysis.
[58] Computational Social Science in a Social World: Challenges and ... — This historical context sets the stage for understanding how today’s digital revolution, driven by advancements in Artificial intelligence (AI), Machine Learning (ML), and Data Science, presents new opportunities and challenges for studying social life. This paradigmatic shift means leveraging new tools to study complex social phenomena, understanding how these technologies shape social life, and a reevaluation of existing social science research (Hindman, 2015). As we reflect on the advancements and applications of AI, ML, and Data Science, we should think very seriously about the role of these technologies in understanding and shaping social policy. Engaging with AI, ML, and Data Science enables us to seize new avenues of research but also places us at the forefront of the risks and challenges of working with such technologies.
[85] Computational Social Science - Microsoft Research: Overview — Overview People Publications Projects Career opportunities News & features ... Lying at the intersection of computer science, statistics and the social sciences, the emerging field of computational social science fills this role, using large-scale demographic, behavioral and network data to investigate human activity and relationships.
[86] Computational Social Science - Penn Computer & Information Science ... — Computational Social Science – Penn Computer & Information Science Highlights Computational Social Science is an interdisciplinary research area that combines social science with computational methods to analyze and model social phenomena. By applying algorithms, simulations, and data analytics, computational social scientists can uncover patterns that traditional social science methods might not capture, enabling a more nuanced understanding of social processes on a large scale. Key areas of focus within computational social science include network analysis, where researchers study how individuals or groups are connected and how information flows through social networks, and behavioral modeling, which involves predicting or simulating human behavior in various contexts such as economics, politics, and online communities. https://highlights.cis.upenn.edu/category/research/computational-social-science
[90] Computational Social Science and Sociology | Annual Reviews — The integration of social science with computer science and engineering fields has produced a new area of study: computational social science. This field applies computational methods to novel sources of digital data such as social media, administrative records, and historical archives to develop theories of human behavior. We review the evolution of this field within sociology via
[91] Social influence research in consumer behavior: What we learned and ... — – A hybrid systematic literature review Author links open overlay panelRamulu Bhukya a, Justin Paul b c Show more Add to Mendeley Share Cite https://doi.org/10.1016/j.jbusres.2023.113870Get rights and content Abstract Social influence plays a significant role in shaping consumer behavior, and research in this area comprises a substantial portion of the literature. Despite the vast number of studies conducted over the decades, no comprehensive evaluation of the current state of research or potential gaps for future investigation has been performed. Therefore, the primary objective of this study is to conduct a hybrid systematic literature review to provide an overview of the current status of research on social influence in consumer behavior employing bibliometric analysis. The interaction with the social environment greatly influences their purchase and consumption behaviors.
[93] Generative AI for Consumer Behavior Prediction: Techniques and ... - MDPI — All Journals Journal of Composites Science (J. : Generative AI techniques, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers, have revolutionized consumer behavior prediction by enabling the synthesis of realistic data and extracting meaningful insights from large, unstructured datasets. This study aims to investigate how generative AI models can effectively enhance consumer behavior prediction and their implications for real-world applications in marketing and customer engagement. Specifically, transformer models excel at processing complicated sequential data for real-time consumer insights, while GANs and VAEs are effective in generating realistic data and predicting customer behaviors such as churn and purchasing intent. Overview of studies on generative AI models for energy data applications. "Generative AI for Consumer Behavior Prediction: Techniques and Applications" Sustainability 16, no.
[113] THE IMPACT OF AI AND BIG DATA ON CONSUMER BEHAVIOR ANALYSIS - ResearchGate — AI encompasses a range of technologies, including machine learning, natural language processing, and neural networks, which can analyze consumer data to uncover patterns and predict future behavior.
[114] Impact of Big Data Analytics for Efficient Consumer Behavior Prediction — Big data analytics plays a critical role in predicting customer behavior by analyzing large and complex data sets from various sources. By applications of predictive modelling principles, it will be acting as a weapon for uncovering patterns and insightful for leveraging from the consumer's point of view. In our study, we incur sincere efforts to discuss the current trends in the domain of
[115] Big Data Analytics and Its Impact on Customer Behavior Prediction in ... — The integration of Big Data Analytics (BDA) in retail has revolutionized customer behavior prediction, enabling businesses to personalize offerings and enhance customer satisfaction. This study aims to assess the impact of Big Data Analytics on customer behavior prediction in retail businesses, exploring the relationship between data-driven insights and retail strategies.
[116] Predictive analytics in customer behavior: Anticipating trends and ... — Predictive analytics in customer behavior: Anticipating trends and preferences - ScienceDirect Predictive analytics in customer behavior: Anticipating trends and preferences In the current work, various machine learning algorithms such as Decision Tree (DT), Random Forest (RT), Logistic Regression (LR), Support Vector Machines (SVM), and gradient boosting are used to predict customer behavior. The results emphasize RT and LR's good performance, while the values of 0.620, 1, 0.766, and 0.878 for the precision, recall, F1-score, and ROC-AUC score outperform the rest. The novelty of this work lies in employing a comprehensive set of machine learning algorithms to predict customer behavior, with a particular emphasis on the superior performance of RF and LR models, as demonstrated by their high precision, recall, F1-score, and ROC-AUC values. For all open access content, the relevant licensing terms apply.
[117] Ethical Considerations in Big Data Analytics | OxJournal — This section explores the key ethical issues, including privacy concerns related to the collection and use of personal data, the need for accuracy and transparency in data handling and analysis, challenges surrounding data accessibility and ownership, and the potential for bias and unfairness in algorithmic decision making. Available at: https://www.ada-asia.com/insights/big-data-improves-customer-experience [Accessed: 20th August 2024]. Available at: https://www.researchgate.net/publication/284679162_Business_Intelligence_and_Analytics_From_Big_Data_to_Big_Impact [Accessed on 30 August 2024]. Available at: https://hbr.org/2013/04/the-hidden-biases-in-big-data [Accessed on 21 August 2024]. Available at: https://improvado.io/blog/big-data-analytics-privacy-problems#:~:text=Data%20privacy%2C%20often%20interchangeably%20used,from%20misuse%20and%20unauthorized%20access [Accessed: 24th August 2024]. PrivacyEnd. Available at: https://www.privacyend.com/transparency-essential-age-big-data/ (Accessed: 24 August, 2024). Available at: https://jake-jorgovan.com/blog/big-data-analytics-transforming-decision-making-in-healthcare-businesses (Accessed: August 31, 2024). Available at: https://www.forbes.com/sites/bernardmarr/2018/05/28/starbucks-using-big-data-analytics-and-artificial-intelligence-to-boost-performance/ [Accessed: August 31, 2024]. Available at: https://vivekjadhavr.medium.com/how-did-netflix-use-big-data-to-transform-their-company-and-dominate-the-streaming-industry [Accessed: 20th August 2024]. Available at: https://www.ibm.com/think/insights/how-to-manage-complexity-and-realize-the-value-of-big-data [Accessed: 29th August 2024]. Available at: https://www.researchgate.net/publication/361529546_THE_ROLE_OF_BIG_DATA_IN_BUSINESS_AND_DECISION_MAKING [Accessed: 20th August 2024].
[118] (PDF) Ethical Implications of Big Data Analytics - ResearchGate — Abstract: This dissertation delves into the ethical considerations associated with the expanding realm of big data analytics in today's society.
[119] Ethical Challenges Posed by Big Data - PMC — Key ethical concerns raised by Big Data research include respecting patient’s autonomy via provision of adequate consent, ensuring equity, and respecting participants’ privacy. Despite these efforts to improve and uphold consent in traditional research, the Revised Common Rule leaves an avenue for avoiding informed consent in Big Data research; the Revised Common Rule requires only broad consent when publicly available information is used, and no consent is required when deidentified information is used.14 Broad consent and lack of consent means that participants are not being provided a complete understanding of the uses of their data. When Big Data researchers are using de-identified publicly available information, no consent is required from the research participants.
[128] Computational Social Science - Penn Computer & Information Science ... — Computational Social Science – Penn Computer & Information Science Highlights Computational Social Science is an interdisciplinary research area that combines social science with computational methods to analyze and model social phenomena. By applying algorithms, simulations, and data analytics, computational social scientists can uncover patterns that traditional social science methods might not capture, enabling a more nuanced understanding of social processes on a large scale. Key areas of focus within computational social science include network analysis, where researchers study how individuals or groups are connected and how information flows through social networks, and behavioral modeling, which involves predicting or simulating human behavior in various contexts such as economics, politics, and online communities. https://highlights.cis.upenn.edu/category/research/computational-social-science
[130] Computational social science - Wikipedia — Computational social science - Wikipedia Computational social science It has been applied in areas such as computational economics, computational sociology, computational media analysis, cliodynamics, culturomics, nonprofit studies. It focuses on investigating social and behavioral relationships and interactions using data science approaches (such as machine learning or rule-based analysis), network analysis, social simulation and studies using interactive systems. Computational social science articles are published across several journals, such as New Media & Society, Social Science Computer Review, PNAS, Political Communication, EPJ Data Science, PLOS One, Sociological Methods & Research and Science. Journal of Computational Social Science "Computational social science". Computational social science. Computational Social Science and Sociology. Wikimedia Commons has media related to Computational social science. Retrieved from "https://en.wikipedia.org/w/index.php?title=Computational_social_science&oldid=1277997148" Computational social science Computational social science
[132] Enhancing Data Quality through Simple De-duplication: Navigating ... — Ensuring high data quality usually involves careful data management and processing such as data validation and cleaning ... Their work highlighted the importance of data cleaning methods such as addressing missing values and correcting mislabels in enhancing classifier predictions. ... Computational social science: Obstacles and opportunities
[133] Data Quality Measures for Computational Research: Ensuring Informed ... — The advertising community needs ways to evaluate the quality of CA data. Although traditional frameworks for evaluating quality are still relevant, they must be updated for these new conditions. Data quality discussions are actively occurring in other fields, including marketing, machine learning, and computational social science.
[135] Methodologies for data quality assessment and improvement — The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques.
[141] 7 Approaches to Data: Qualitative, Quantitative and Triangulation — In this module you will learn about the quantitative and qualitative approaches to data and the utility of mixing those methods for collection and analysis of data. As triangulation of methods is a significant strategy in social science research these days, you will be introduced to different advantages and limitations of mixing methods in
[142] Qualitative Study - StatPearls - NCBI Bookshelf — For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or the participants' environment, depending on the study goals and design. Qualitative research uses techniques including structured and unstructured interviews, focus groups, and participant observation not only to help generate hypotheses that can be more rigorously tested with quantitative research but also to help researchers delve deeper into the quantitative research numbers, understand what they mean, and understand what the implications are.
[152] Pathways Between Social Science and Computational Social Science - Springer — Pathways Between Social Science and Computational Social Science: Theories, Methods, and Interpretations | SpringerLink Demonstrates theoretical, methodological and topical pathways between traditional and computational social science Part of the book series: Computational Social Sciences (CSS) The first part exemplifies how new theoretical models and approaches on which CSS research is based arise from theories of social science. The expected readership of the volume includes researchers with a traditional social science background who wish to approach CSS, experts in CSS looking for substantive links to more traditional social science theories, methods and topics, and finally, students working in both fields. Computational Social Science (2009): Computational Social Science computational social science models Book Title: Pathways Between Social Science and Computational Social Science Series Title: Computational Social Sciences
[153] Understanding the paradigm shift to computational social science in the ... — The era of big data has created new opportunities for researchers to achieve high relevance and impact amid changes and transformations in how we study social science phenomena. The changing costs of data collection and the new capabilities that researchers have to conduct research that leverages micro-level, meso-level and macro-level data suggest the possibility of a scientific paradigm shift toward computational social science. Section 3 describes the new paradigm in the era of big data, and how it relates to decision support, IS and social science research. We next explore three representative areas of research that now involve the use of big data and analytics for business, consumer and social insights: Internet-based selling and pricing; social media and social
[154] PDF — • Approaches reducing representation, sampling, and measurement errors of digital data • Studies substituting more traditional data collections (e.g., web surveys) with digital data (e.g., measuring opinions with digital traces) • Studies that go beyond the pure tracking (or donating) of app, search term, and URL data,
[155] Computational Social Science - Penn Computer & Information Science ... — Computational Social Science – Penn Computer & Information Science Highlights Computational Social Science is an interdisciplinary research area that combines social science with computational methods to analyze and model social phenomena. By applying algorithms, simulations, and data analytics, computational social scientists can uncover patterns that traditional social science methods might not capture, enabling a more nuanced understanding of social processes on a large scale. Key areas of focus within computational social science include network analysis, where researchers study how individuals or groups are connected and how information flows through social networks, and behavioral modeling, which involves predicting or simulating human behavior in various contexts such as economics, politics, and online communities. https://highlights.cis.upenn.edu/category/research/computational-social-science
[172] Computational politics - Wikipedia — Computational politics is the intersection between computer science and political science. The area involves the usage of computational methods, such as analysis tools and prediction methods, to present the solutions to political sciences questions.
[173] Computational Social Science and the Study of Political Communication — The challenge of disentangling political communication processes and their effects has grown with the complexity of the new political information environment.
[180] Computational vs. qualitative: analyzing different approaches in ... — Social researchers have traditionally used qualitative methods such as thematic and content analysis in frame detection. However, with the advent of large text datasets, automated techniques, e.g. natural language processing (NLP), outperform traditional text analyses through their ability to analyze large text data with minimal time and effort
[191] Computational Social Science for Policy and Quality of Democracy ... — Computational Social Science for Policy and Quality of Democracy: Public Opinion, Hate Speech, Misinformation, and Foreign Influence Campaigns - NYU’s Center for Social Media and Politics Computational Social Science for Policy and Quality of Democracy: Public Opinion, Hate Speech, Misinformation, and Foreign Influence Campaigns Computational Social Science for Policy and Quality of Democracy: Public Opinion, Hate Speech, Misinformation, and Foreign Influence Campaigns In this review, I examine the questions that computational social scientists are attempting to answer – as well as the tools and methods they are developing to do so – in three areas where the rise of social media has led to concerns about the quality of democracy in the digital information era: online hate; misinformation; and foreign influence campaigns.
[192] Computational Social Science for Policy and Quality of Democracy ... — In this review, I examine the questions that computational social scientists are attempting to answer – as well as the tools and methods they are developing to do so – in three areas where the rise of social media has led to concerns about the quality of democracy in the digital information era: online hate; misinformation; and foreign influence campaigns. With this basic background on the ways in which Computational Social Science can be utilized to measure public opinion using social media data, in the remainder of this chapter, I examine the potential of Computational Social Science to address three pernicious forms of online behaviour that have been identified as threats to the quality of democracy: hate speech, misinformation, and foreign influence campaigns.
[193] Computational Propaganda: Political Parties, Politicians, and Political ... — Our methodology in this work has been purposefully mixed, we make use of quantitative analysis of data from several social media platforms and qualitative work that includes interviews with the people who design and deploy political bots and disinformation campaigns.
[194] Electoral Information Toolkit - The Center for Information, Technology ... — This toolkit is designed to help you identify, understand, and counter electoral misinformation. It provides practical resources, such as tools for fact-checking, case studies on real-world misinformation campaigns, and strategies for promoting credible information. By empowering you with knowledge and actionable steps, this toolkit supports a more informed public, helping to protect the
[206] Computational Social Science - Penn Computer & Information Science ... — Computational Social Science – Penn Computer & Information Science Highlights Computational Social Science is an interdisciplinary research area that combines social science with computational methods to analyze and model social phenomena. By applying algorithms, simulations, and data analytics, computational social scientists can uncover patterns that traditional social science methods might not capture, enabling a more nuanced understanding of social processes on a large scale. Key areas of focus within computational social science include network analysis, where researchers study how individuals or groups are connected and how information flows through social networks, and behavioral modeling, which involves predicting or simulating human behavior in various contexts such as economics, politics, and online communities. https://highlights.cis.upenn.edu/category/research/computational-social-science
[209] Fostering interdisciplinary collaboration in computational diplomacy: A ... — Fostering interdisciplinary collaboration in computational diplomacy: A multi-layered network approach to improve our understanding of institutional complexity and effective governance design - ScienceDirect Fostering interdisciplinary collaboration in computational diplomacy: A multi-layered network approach to improve our understanding of institutional complexity and effective governance design This article delineates a complexity and data driven approach to represent governance systems as multi-layered networks. Such representation is useful to foster interdisciplinary collaborations between researchers working in global governance/international relations and data science/computational science. The combination of a data-driven approach with computational modelling paves the way to both contribute to a more fundamental understanding of how multilateral governance systems work and to address some important contemporary questions about institutional complexity and the effectiveness of governance design.
[210] Full article: The Future of Interdisciplinary Research in the Digital ... — Our findings derived from one-on-one interviews (n = 22) reinforce the importance of interdisciplinary collaboration and highlight the significance of "interpreters," i.e., individuals able to communicate with and connect various areas of science, education, and academic institutions' role in enhancing interdisciplinary collaborations of
[214] An Appraisal of Social Network Theory and Analysis as Applied ... - PubMed — Recently developed network analysis techniques, technological innovations in communication, and changes in theoretical perspectives to include a focus on social and environmental behavioral influences have created opportunities for new theory and ever broader application of social networks to public health topics.
[217] Qualitative Coding in the Computational Era: A Hybrid Approach to ... — Yet standard computational approaches do not neatly align with traditional qualitative practices. The authors introduce a hybrid hum … Sociologists have argued that there is value in incorporating computational tools into qualitative research, including using machine learning to code qualitative data.
[221] Analytical sociology and computational social science - PMC — From the perspective of the social sciences, references to social laws appear unfounded and misplaced, however, and in this article we outline how analytical sociology, with its theory-grounded approach to computational social science, can help to move the field forward from mere descriptions and predictions to the explanation of social phenomena.
[222] Computational social science: Obstacles and opportunities | Science - AAAS — The field of computational social science (CSS) has exploded in prominence over the past decade, with thousands of papers published using observational data, experimental designs, and large-scale simulations that were once unfeasible or unavailable to researchers. ... These studies have greatly improved our understanding of important phenomena
[223] Understanding the paradigm shift to computational social science in the ... — The era of big data has created new opportunities for researchers to achieve high relevance and impact amid changes and transformations in how we study social science phenomena. The changing costs of data collection and the new capabilities that researchers have to conduct research that leverages micro-level, meso-level and macro-level data suggest the possibility of a scientific paradigm shift toward computational social science. Section 3 describes the new paradigm in the era of big data, and how it relates to decision support, IS and social science research. We next explore three representative areas of research that now involve the use of big data and analytics for business, consumer and social insights: Internet-based selling and pricing; social media and social
[227] How different strengths of ties impact project performance in ... — Purpose - The strength of ties between individuals influences the speed and spread of crisis information dissemination (CID). By constructing networks of strong and weak ties, this paper aims to innovatively explore the impacts of strong and weak ties on the CID at the macro level.
[229] The tie effect on information dissemination: the spread of a commercial ... — Second, previous studies have argued for the role of weak ties in diffusing market information. Weak ties tend to be local bridges to different social circles and presumably possess heterogeneous and more useful resources (Burt, 1992, Granovetter, 1973, Lin, 1982, Montgomery, 1992). However, strong ties are found to serve as network bridges in
[235] Computational Social Science and Sociology - PubMed — The integration of social science with computer science and engineering fields has produced a new area of study: computational social science. This field applies computational methods to novel sources of digital data such as social media, administrative records, and historical archives to develop theories of human behavior.
[236] The Future of Computational Social Science - Oxford Academic — This chapter outlines the current state and future trajectory of computational social science (CSS), a growing field combining tools and techniques from computer science with theories and methods from the social sciences. This fusion of approaches offers enormous possibilities to expand understanding of human society at scale.
[237] Computation and the Sociological Imagination - James Evans, Jacob G ... — Social theory is traditionally expressed in natural language, using a rich conceptual vocabulary. It pays for nuance with ambiguity. A rich theory can fit almost any outcome; in explaining everything, it risks explaining nothing. Computational sociologists often express theory in the language of mathematics or algorithm.
[249] Computational social science: Obstacles and opportunities | Science - AAAS — Unmentioned challenges to computational social sciences in future. Zhuo Zhang. Master Student; Central South University; Peng Lu. ... For one of the main data in the field of computational social sciences, namely social network data, it is non-Euclidean data, so that the above-mentioned mature deep learning technology cannot be directly applied
[254] PDF — Key Words: computational social sciences, sociology, social simulation, experiments, big data Category: E.0, I.6, J.4 1 Introduction Social sciences may appear to the casual observer as deeply rooted in a somewhat old-fashioned intellectual tradition, often — even if not necessarily — based on qualitative studies and leading more to long-enduring philosophical debates than to the progressive knowledge accumulation typical of the natural sciences. 6. To allow for the realization of innovative, larger-scale CSS projects, institu-tional infrastructures (e.g., dedicated research centres) should be developed allowing social scientists to communicate and work together with scholars from computer sciences, physics, mathematics and other scientific disciplines.
[261] Computational social science: Obstacles and opportunities - ResearchGate — These data present conceptual, computational and ethical challenges that require a rejuvenation of our scientific theories to keep up with the rapidly changing social realities and our capacities
[265] Computational social science: Obstacles and opportunities - ResearchGate — While survey data is constructed for processing through variable-based analysis, requiring pre-compartmentalized data designed to be palatable for a scientific perspective that sees the social
[267] Integrating explanation and prediction in computational social ... - Nature — The combination of computational and social sciences requires the integration of explanatory and predictive approaches into 'integrative modelling', according to Hofman and colleagues.
[293] PDF — Computational Social Science: Exciting Progress and Future Challenges Duncan Watts Microsoft New York City, NY duncan@microsoft.com ABSTRACT The past 15 years have witnessed a remarkable increase in both the scale and scope of social and behavioral data available to researchers, leading some to herald the emergence of a new field
[294] Computational Social Science - Penn Computer & Information Science ... — Computational Social Science – Penn Computer & Information Science Highlights Computational Social Science is an interdisciplinary research area that combines social science with computational methods to analyze and model social phenomena. By applying algorithms, simulations, and data analytics, computational social scientists can uncover patterns that traditional social science methods might not capture, enabling a more nuanced understanding of social processes on a large scale. Key areas of focus within computational social science include network analysis, where researchers study how individuals or groups are connected and how information flows through social networks, and behavioral modeling, which involves predicting or simulating human behavior in various contexts such as economics, politics, and online communities. https://highlights.cis.upenn.edu/category/research/computational-social-science
[295] Computational Social Science: Big Data Analytics for Societal Insights — Computational Social Science: Big Data Analytics for Societal Insights | Graph AI Computational Social Science: Big Data Analytics for Societal Insights Computational Social Science (CSS) leverages big data analytics to decode complex social phenomena, enabling researchers to unearth insights that were previously inaccessible. This symbiotic relationship between data analysis and practical application highlights the transformative potential of Computational Social Science in both research and real-world scenarios. This holistic approach is vital in building a more equitable framework for computational social science, where the benefits of data-driven insights are accessible to all segments of society. Artificial intelligence and machine learning are set to revolutionize Computational Social Science by providing tools that can process, analyze, and interpret data in ways that humans alone cannot.
[296] Social Networks Analysis and Machine Learning: an Overview of ... — A significant data mining challenge is social network analysis. To effectively retain the network topological structure and other attribute information, it is essential to encode network data into low-dimensional representations, or network embeddings, before doing social network analysis. Classification, link prediction, anomaly detection, and clustering are further applications made easier
[297] Application of Machine Learning Models in Social Sciences ... - MDPI — Keywords: machine learning in social sciences; nonlinear relationships; model interpretability; predictive analytics; imbalanced data handling The effectiveness of machine learning models in social science research depends on their ability to capture nonlinear relationships and how well they generalize to new, unseen data. Model evaluation, validation, and handling of imbalanced data are integral to applying machine learning in social science research. One of the primary challenges in machine learning, particularly in social science, is interpreting complex models like neural networks or ensemble methods such as random forests and GBMs. While these models offer high predictive accuracy, they are often called “black boxes” due to the difficulty in explaining their internal decision-making processes.
[298] PDF — The rapid growth of the social networks observed several key challenges such as data gathering techniques, heterogeneity, scalability, missing data etc. The amount and kinds of data generated by social network usage are too rich to be captured by only one of these methods. The data may be collected from OSNs, (i) from the social network
[299] Literature Review: Combining Machine Learning with Social Network ... — The symbiotic relationship between Social Network Analysis (SNA) and Machine Learning (ML) emerges as an indispensable tool for recognizing and decoding complex patterns. This paper provides an in-depth exploration of the state-of-the-art methodologies employed by researchers, addressing key research questions concerning the integration of SNA
[300] Social Network Analysis in AI - Restackio — In the realm of social network analysis, the integration of AI methodologies has proven to be transformative. By leveraging advanced techniques such as reinforcement learning and graph neural networks (GNNs), researchers are able to address complex challenges inherent in social networks. ... Physics-Informed Machine Learning: By integrating
[301] Computational social science and social computing | Machine Learning — Computational social science is an emerging research area at the intersection of computer science, statistics, and the social sciences, in which novel computational methods are used to answer questions about society. The field is inherently collaborative: social scientists provide vital context and insight into pertinent research questions, data sources, and acquisition methods, while
[306] Pathways Between Social Science and Computational Social Science - Springer — Pathways Between Social Science and Computational Social Science: Theories, Methods, and Interpretations | SpringerLink Demonstrates theoretical, methodological and topical pathways between traditional and computational social science Part of the book series: Computational Social Sciences (CSS) The first part exemplifies how new theoretical models and approaches on which CSS research is based arise from theories of social science. The expected readership of the volume includes researchers with a traditional social science background who wish to approach CSS, experts in CSS looking for substantive links to more traditional social science theories, methods and topics, and finally, students working in both fields. Computational Social Science (2009): Computational Social Science computational social science models Book Title: Pathways Between Social Science and Computational Social Science Series Title: Computational Social Sciences
[307] Integrating Computer Prediction Methods in Social Science: A Comment on ... — For example, the shift to open science leads social scientists to embrace methods insulating against analytical flexibility (Nosek et al., 2018) while computer scientists use crowdsourcing, such as the "common task framework," to achieve larger modeling goals (Breznau, 2021b). 2 Cross-integration of these practices could help both types of
[308] Computational social science: Obstacles and opportunities | Science - AAAS — Mechanisms and causality (also called theory) play a central role in social sciences (13). However, computational social science may end traditional theories (14, ), because they also care about relevant rules (15). How to effectively combine causality and prediction in computational social sciences? There is still a long way to go. References