Computational Sociology Research Papers - Academia.edu (original) (raw)

2025, Technological Forecasting and Social Change

If the global brain is a suitable model of the future information society, then one future of research in this global brain will be in its past, which is its distributed memory. In this paper, we draw on Francis Heylighen, Marta Lenartowicz, and Niklas Luhmann to show that future research in this global brain will have to reclaim classical theories of social differentiation in general and theories of functional differentiation in particular to develop higher resolution images of this brain's function and sub-functions. This claim is corroborated by a brain wave measurement of a considerable section of the global brain. We used the Google Ngram Viewer, an online graphing tool which charts annual counts of words or sentences as found in the largest available corpus of digitalized books, to analyse word frequency time-series plots of key concepts of social differentiation in the English as well as in the Spanish, French, German, Russian, and Italian sub-corpora between 1800 and 2000. The results of this socioencephalography suggest that the global brain's memory recalls distinct and not yet fully conscious biases to particular sub-functions, which are furthermore not in line with popular trend statements and self-descriptions of modern societies. We speculate that an increasingly intelligent global brain will start to critically reflect upon these biases and learn how to anticipate or even design its own desired futures.

2025, Routledge eBooks

We propose that late modern policing practices, that rely on neighbourhood intelligence, the monitoring of tensions, surveillance and policing by accommo-dation, need to be augmented in light of emerging 'cyber-neighbourhoods', namely social media networks. The 2011 riots in England were the first to evidence the widespread use of social media platforms to organise and respond to disorder. The police were ill-equipped to make use of the intelligence emerging from these non-terrestrial networks and were found to be at a disadvantage to the more tech-savvy rioters and the general public. In this paper, we outline the development of the 'tension engine' component of the Cardiff Online Social Media ObServatroy (COSMOS). This engine affords users with the ability to monitor social media data streams for signs of high tension which can be analysed in order to identify deviations from the 'norm' (levels of cohesion/low tension). This analysis can be overlaid onto a palimpsest of curated data, such as official statistics about neighbourhood crime, deprivation and demography, to provide a multidimensional picture of the 'terrestrial' and 'cyber' streets. As a consequence, this 'neighbourhood informatics' enables a means of questioning official constructions of civil unrest through reference to the user-generated accounts of social media and their relationship to other, curated, social and economic data.

2025

Harnessing social media data for social science research entails creating measures out of the largely unstructured, noisy data that users generate on different platforms. This harnessing, particularly of data at scale, requires using methods developed in computer science. But it also typically requires integrating these methods with assessments of measurement quality along social science criteria -- reliability, validity and unbiasedness. In this paper, we outline measurement issues that arise when using social media data. We show examples of how to construct measures and discuss different measurement considerations and best practices. We conclude with a discussion of ways to accelerate research in this space, highlighting contributions that can be made by both social scientists and computer scientists.

2025, International Journal of Parallel, Emergent and Distributed Systems

The growing number of people using social media to publish their opinions, share expertise, make social connections and promote their ideas to an international audience is creating data on an epic scale. This enables social scientists to conduct research into ethnography, discourse analysis and analysis of social interactions, providing insight into today's society, which is largely augmented by social computing. The tools available for such analysis are often proprietary and expensive, and often noninteroperable, meaning the rapid marshalling of large data-sets through a range of analyses is arduous and difficult to scale. The collaborative online social media observatory (COSMOS), an integrated social media analysis tool is presented, developed for open access within academia. COSMOS is underpinned by a scalable Hadoop infrastructure and can support the rapid analysis of large data-sets and the orchestration of workflows between tools with limited human effort. We describe an architecture and scalability results for the computational analysis of social media data, and comment on the storage, search and retrieval issues associated with massive social media data-sets. We also provide an insight into the impact of such an integrated ondemand service in the social science academic community.

2025, Policing and Society

2025

This article examines the co-evolution of players ’ individual performance and their interaction network in a Massively Multiplayer Online Game (MMOG). The objective is to test whether the application of theories from the real world is valid in virtual worlds. While the results indicate that the structural effects and demographic variables active in the real world influence the evolution of the players ’ interaction network in MMOGs (e.g. transitivity, reciprocity, and homophily), they do not provide evidence that players ’ structural embeddedness in the interaction network influences player performance. These findings have important implications for researchers and practitioners who need to understand social processes in MMOGs (e.g., when launching marketing campaigns in MMOGs) or who study MMOGs and then use their findings to draw conclusions about the real world (e.g., when analyzing the relationship between employee performance and network structure).

2025

Esta obra colectiva tiene como objetivo explorar las posibilidades epistemológicas y metodológicas de la sociología computacional a través de estudios de caso, reflexiones teóricas y propuestas aplicadas. El enfoque central del libro se... more

2025, Royal Society Open Science

This study investigates researcher variability in computational reproduction, an activity for which it is least expected. Eighty-five independent teams attempted numerical replication of results from an original study of policy preferences and immigration. Reproduction teams were randomly grouped into a ‘transparent group’ receiving original study and code or ‘opaque group’ receiving only a method and results description and no code. The transparent group mostly verified original results (95.7% same sign and p-value cutoff), while the opaque group had less success (89.3%). Second decimal place exact numerical reproductions were less common (76.9 and 48.1%). Qualitative investigation of the workflows revealed many causes of error, including mistakes and procedural variations. When curating mistakes, we still find that only the transparent group was reliably successful. Our findings imply a need for transparency, but also more. Institutional checks and less subjective difficulty for researchers ‘doing reproduction’ would help, implying a need for better training. We also urge increased awareness of complexity in the research process and in ‘push button’ replications.

2025, Proceedings of the 3rd International Web Science Conference

Massively multiplayer online games (MMOGs) maintain archival databases of all player actions and attributes including activity by accounts engaged in illicit behavior. If individuals in online worlds operate under similar social and psychological motivations and constraints as the offline world, online behavioral data could inform theories about offline behavior. We examine high risk trading relationships in a MMOG to illuminate the structures online clandestine organizations employ to balance security with efficiency and compare this to an offline drug trafficking network. This data offers the possibility of performing social research on a scale that would be unethical or impracticable to do in the offline world. However, analyzing and generalizing from clandestine behavior in online settings raises complex epistemological and methodological questions about the validity of such mappings and what methods and metrics are appropriate in these contexts. We conclude by discussing how computational social science can be applied to online and offline criminological concerns and highlight the "dual use" implications of these technologies.

2025, Temas selectos para las ciencias sociales computacionales Contribuciones desde América Latina

Este libro ha sido sometido al proceso de revisión de pares por el método de referato doble ciego. Esta obra se encuentra protegida por derechos de autor © Antonio Aguilera Ontiveros y Norma Leticia Abrica Jacinto y se distribuye bajo Licencia Creative Commons Atribución -No Comercial -Compartir Obras Derivadas Igual 4.0 Internacional. Usted es libre de compartir, copiar, distribuir, ejecutar y comunicar públicamente la obra, hacer obras derivadas bajo las siguientes condiciones: Atribución -Debe reconocer los créditos de la obra de la manera especificada por el autor o el licenciante (pero no de una manera que sugiera que tiene su apoyo o que apoyan el uso que hace de su obra). No Comercial -No puede utilizar esta obra para fines comerciales. Compartir bajo la Misma Licencia -Si altera o transforma esta obra, o genera una obra derivada, sólo puede distribuir la obra generada bajo una licencia idéntica a ésta.

2025

In this following comment, we present some criticisms that were made before in the social networks field that might help develop the field of computational social science. For this, we illustrate our argument considering the usage of social networks in the Population-Scale Network Analysis (POPNET) project through the presentation of Frank Takes at the 7th International Conference on Computational Social Science (IC2S2). The main argument is that more attention should be given to the theoretical assumptions to motivate, refine and explore more profound relevant research questions of interest to social scientists. We comment on using small-world research to identify how different research questions can be further explored.

2025, Universidade Federal do Espírito Santo

Às minhas avós Amabele e Alayrmulheres de ternura e coração firme. À minha mãe, meu pai e meu irmão pelo balaio amoroso. FORA TEMER! À minha mãe, Marlenea maior mulher do mundo. O ombro, o colo e a mão de amor. Ao meu pai, Diomedes, acordar antes das 6h, tomar da vida o caule de sustentação, e olhar para frente. Ao meu irmão, Julio César, meu irmão maior, grande coração, esse trabalho é nosso. À minha família, todo desse laço de aconchego, o campo seguro para perseverar, o retorno ao mar. Agradeço às minha professoras & investigadoras mulheres, outras pesquisadoras que cruzaram o meu caminho mostrando que o trajeto da pesquisa pode ser cultivado. Dentre essas, Ana Targina Rodrigues Ferraz, minha primeira orientadora de iniciação científica. Francis Sodré, minha orientadora do trabalho final da graduação. E a professora e convidada da banca examinadora, Daniela Zanetti, também coordenadora da pós-graduação nesses primeiros anos. Ao meu orientador, mentor e mago, Fábio Malini, mais do que abrir portas, me contou sobre mundos possíveis. Quando mal sabia eu sobre as possibilidades da comunicação, Fábio mostrou que a cibercultura podia receber uma deslocada. Aos amigos e ativistas das redes e das ruas, a mobilização nos une num tempo de afetação e reciprocidade. Das marchas, dos foras e dos confetes & bandeirasnão vamos nos silenciar diante do golpe de tristeza. Continuaremos cantando por um país sem machismo, racismo, sem homofobia, lesbofobia e transfobia, contra a falsa guerras às drogas, pela descriminalização do aborto, pelos refugiados, pelos indígenas e quilombolas, pelas questões climáticas urgentes, pelos involuntários da pátria! Aos Programa de Pós Graduação em Comunicação e Territorialidades, as secretárias Paula e Simone. Ao corpo docente, toda composição de pesquisa e ensino que fortalecem esse mestrado. E, principalmente, aos colegas de mestrado pela união entre as aulas e conversas de corredor, todos compartilhamos dessa história. Ao LABIC (Laboratório de Imagem e Cibercultura/UFES), potente território de ocupação e criação multidisciplinar. Um agradecimento especial ao Fabio Goveia, coordenador em bom humor na pesquisa acadêmica. À Adriana Ilha, coordenadora que nesses últimos meses tem sido parceira de escrita durante os longos dias de sol na UFES. Ao coordenador Patrick Ciarelli, seriedade e disciplina desvendando os mistérios da computação para o povo das humanidades. A todos os labiqueiros, pesquisadores que colaboraram com a minha formação nesse campo híbrido da cibercultura, aos dias de Hackday e ativismo. Vida longa ao LABIC! Aos professor Henrique Antoun, referência intelectual nesse processo de pesquisa e, agora, orientador no doutorado da ECO-UFRJ. Obrigada por participar da banca e topar o desafio! À Laura porque me contou da amizade como modo de vida. À Kamilla, a companheira de coração e calma, transbordaram os dois anos de vivências juntas numa casa lindaangústias e alegriasde uma vida inteira. À Raisa, aterrisou na hora certa e me lembrou que carisma é tudo. À Giovanna, pelo fôlego e ar nos pulmões dessa reta final. Ao amigo-parceiro-afeto Fabrício Fernandez, o olhar fraterno e a mão calejada da escritura me apontando para a vida. Obrigada amigo! À Paulinha, amiga de aventuras antropológicas e longas horas de confusão e risadas, nossos equívocos no exercício etnográfico podem muito. Um beijo! Aos queridos amigos de longas conversas noturnas e tardes de culinária & café, nos últimos anos no Centro da cidade de Vitória. Ao Anselmo Clemente, de algum modo sempre presente me instigando. Haroldo, o inconformado amigo, me levantou sempre que necessário. Ao Allan Menegassi, parente e parceiro de imaginários cosmopolíticos. À Eve Flores, querida amiga de lutas incansáveis. O querido André Alves pelo silêncio compartilhado na Kaffa e as conversas sobre física. À Mônica Patrícia, pelo bom humor nos encontros necessários. Ao Sérgio Rodrigo, pelo incentivo e ombro amigo: nunca me disse que seria fácil. Ao Anderson Cacilhas, um amigo do peito e meu grande especulador político. Ao Marcel, uma escuta e acompanhamento imprescindíveis. À Patricia Galetto, colega que topou uma revisão em tempo real. Agradeço a parceria e comprometimento nesses momentos finais. Ao querido Bruno Banzin, acolhedor nessas idas ao Rio para o processo seletivo de doutorado da ECO-UFRJ. Te vejo aí. Aos meus amigos do Twitter, os @s de tantos anos de troca de mensagens sobre tudo e nada, referências de papers, piadas e menes, mobilizações e tretas, eleições e paqueras. Continuemos....@followlori. À Iriny Lopes, a força da mulher na política. Uma guerreira incansável por um Brasil e Espirito Santo mais acolhedor às diferenças. À Lena Azevedo, repórter e amiga que me ensinou sobre o jornalismo e os princípios de uma ética da escuta, relato e da investigação. O compromisso com um mundo justo e atento à nossa época. À FAPES, pela bolsa de pesquisa que tornou possível essa pesquisa e a dedicação ao mestrado. Porque se o senhor parar para pensar, as três leis da robótica são os princípios essenciais de muitos dos sistemas éticos dos sistemas éticos do mundo. É claro que todo humano deve ter o instinto da auto-preservação. E esta é a terceira regra, para um robô. Também todo "bom" humano com uma consciência social e senso de responsabilidade deve atender à autoridade; escutar seu médico, seu governo, seu psiquiatra, seus companheiros homens; obedecer leis; seguir regras, conformar-se aos costumesmesmo quando interferem com seu conforto ou segurança. Esta é a segunda regra, para um robô. E também todo "bom" humano deve amar aos outros como a si mesmo, proteger seus iguais, arriscar sua vida para salvar a de outrem. E esta éa primeira regra para um robô. Para colocar a questão simplesmente, se Byerley segue todas as Leis da Robótica, ele pode ser um robô, e pode simplesmente ser um homem muito bom. Dr. Susan Calvin, coletânia Isaac Asimov 'Nós, Robôs', p. 437 Pode-se dizer, na verdade, que as relações são o que faz as pessoas "verem", o que quer que elas vejam. Marilyn Strathern, O Efeito Etnográfico, p.405 RESUMO É possível falar em uma programação da comunicação humana? Esta dissertação tem como objetivo compreender o papel algorítmico em rede na sociedade contemporânea, principalmente como agentes automatizadosconhecidos como robôs (bots)atuaram no Twitter nas Eleições Presidenciais do Brasil em 2014. Consideramos que, com a quantidade de dados em larga escala (big data), esse agenciamento recíproco entre atores humanos e não humanos é ativado através do processamento de dados e da mediação no ambiente das redes sociais. Sendo assim, ponderamos que o fluxo de dados em grande quantidade é composto em caráter associativo entre agentes humanos e artificiais. Nossa hipótese é que esses atores em relação implicam novas práticas comunicacionais e acabam por transformar os espaços públicos de interação nas redes sociais (como Twitter e Facebook) criando uma cultura da massificação e notificação. Por meio de um método quali-quantitativo que une a cartografia e a teoria dos grafos, é possível coletar, analisar e visualizar os rastros produzidos pelos usuários das redes sociais. Na medida em que dialogamos com referências teóricas na filosofia, antropologia, comunicação, computação e literatura, buscamos compreender a escala em que o bot enquanto ferramenta algorítmica altera o nosso modo de comunicar e as implicações na esfera digital. A nossa análise das Eleições Presidenciais de 2014 tem como base dados coletados do Twitter durante debates eleitorais e a última semana do pleito. Desse modo, nossa discussão problematiza o bot como um agente algorítimico capaz de interferir no modo como o social se faz.

2025, Computational and Mathematical Organization Theory

The ground truth program used simulations as test beds for social science research methods. The simulations had known ground truth and were capable of producing large amounts of data. This allowed research teams to run experiments and ask questions of these simulations similar to social scientists studying real-world systems, and enabled robust evaluation of their causal inference, prediction, and prescription capabilities. We tested three hypotheses about research effectiveness using data from the ground truth program, specifically looking at the influence of complexity, causal understanding, and data collection on performance. We found some evidence that system complexity and causal understanding influenced research performance, but no evidence that data availability contributed. The ground truth program may be the first robust coupling of simulation test beds with an experimental framework capable of teasing out factors that determine the success of social science research.

2025, Social Computing, Behavioral-Cultural Modeling and Prediction

Ethnographic research identifies brokering (a.k.a., "copping for others") as an important and popular way people who use heroin acquire the drug by making purchases for their peers. Brokering is when a customer buys drugs for a fellow customer using the buyer's money and is paid using drug the buyer purchases. This distributes heroin costs. Heroin dealers obviously manipulate price and/or drug purity to make profits and compete for buyers, but a hidden way they alter "price" is by adjusting the size of heroin packages they sell. Using an agent-based model, we simulate brokering and heroin package resizing to understand how these dynamics influence heroin consumption costs. High rates of dealer arrest are tested against these dynamics. Findings indicate the Quantity-Adjusted Price of heroin is greater than its retail price in all conditions, implying increased competition in heroin markets does not lower costs.

2024, arXiv (Cornell University)

Studying corruption presents unique challenges. Recent work in the spirit of computational social science exploits newly available data and methods to give a fresh perspective on this important topic. In this chapter we highlight some of these works, describing how they provide insights into classic social scientific questions about the structure and dynamics of corruption in society from micro to macro scales. We argue that corruption is fruitfully understood as a collective action problem that happens between embedded people and organizations. Computational methods like network science and agent-based modeling can give insights into such situations. We also present various (big) data sources that have been exploited to study corruption. We conclude by highlighting work in adjacent fields, for instance on the problems of collusion, tax evasion, organized crime, and the darkweb, and promising avenues for future work.

2024, arXiv (Cornell University)

In recent years, the rapid advancement of machine learning (ML) models, particularly transformer-based pre-trained models, has revolutionized Natural Language Processing (NLP) and Computer Vision (CV) fields. However, researchers have discovered that these models can inadvertently capture and reinforce social biases present in their training datasets, leading to potential social harms, such as uneven resource allocation and unfair representation of specific social groups. Addressing these biases and ensuring fairness in artificial intelligence (AI) systems has become a critical concern in the ML community. The recent introduction of pre-trained vision-and-language (VL) models in the emerging multimodal field demands attention to the potential social biases present in these models as well. Although VL models are susceptible to social bias, there is a limited understanding compared to the extensive discussions on bias in NLP and CV. This survey aims to provide researchers with a high-level insight into the similarities and differences of social bias studies in pre-trained models across NLP, CV, and VL. By examining these perspectives, the survey aims to offer valuable guidelines on how to approach and mitigate social bias in both unimodal and multimodal settings. The findings and recommendations presented here can benefit the ML community, fostering the development of fairer and non-biased AI models in various applications and research endeavors.

2024

En este trabajo comparamos mediante un modelo basado en agentes de demanda de agua doméstica parametrizado en región metropolitana de Valladolid, el efecto en diferentes escenarios de dos modelos de difusión de comportamiento y opinión respecto a la conservación del recurso alternativos: el modelo de Young-Edwards y un modelo basado en el mecanismo de endorsements. Los resultados muestran que las diferencias surgen en función de los clusters espaciales con comportamiento homogéneo en la región. Este análisis se fundamenta en los programas de gestión de la demanda que tienen como objetivo la modificación de los cambios de comportamiento de los demandantes frente a medidas basadas en un cambio tecnológico. Palabras clave: modelado basado en agentes, consumo de agua doméstica, difusión de comportamiento, simulación, gestión de agua

2024

El objetivo de este trabajo es demostrar que el modelado basado en agentes permite, como paradigma integrador, incorporar y adaptar diferentes aspectos espaciales y socioeconómicos a problemas multidimensionales con alto nivel de realismo. En el dominio específi co de la demanda doméstica de agua se estudia un caso concreto de integración de un modelo basado en agentes híbrido con un sistema de información geográfi co. En el modelo se incorporan submodelos de dinámica urbana y difusión reversible y no reversible * Este trabajo se deriva de la participación de sus autores en los proyectos de investigación fi nanciados por el Ministerio de Educación y Ciencia con referencias DPI2004-06590 y DPI2005-05676, titulados "Integración empresarial y gestión de la cadena de suministro basada en sistemas multiagente" y "Simulador basado en agentes para la gestión del agua en espacios metropolitanos".

2024, The Anthem Companion to Raymond Boudon

Raymond Boudon posits that methodological individualism (hereafter, MI) is an explanatory framework characterized by two integral components: a micro-level analysis centered around rationality (where rationality does not mean necessarily that action must be explained in utilitarian terms) and a macro-level analysis focused on unintended aggregation effects. According to Boudon, this explanatory model aligns with the research practices employed by major social scientists who made substantial scientific contributions. This study unfolds in two parts. The initial segment (the first four sections) delves into a more comprehensive understanding of Boudon's MI by examining its relationship with key themes in social methodology. These themes include the demarcation between scientific and ideologically oriented explanations, the ontology of collective concepts, various forms of rationality, essential aspects of a comprehensive sociological approach, and the discourse surrounding explanations, deductive-nomological models, and mechanisms. The analysis in the first part of this study serves as a foundation for comprehending its second part. The latter, encompassing the last two sections, delves into Boudon's quest to validate the explanatory prowess of MI. Boudon scrutinizes the history of sociology, seeking evidence of implicit applications of MI with paradigmatic significance due to their prominence. According to Boudon, the historical trajectory of sociology attests to MI's capacity to elucidate a diverse range of crucial social phenomena, including social change, ideology, false beliefs, and moral sentiments-issues often traditionally explained through holism or methodological collectivism. Boudon contends that MI found application as an explanatory tool even among classical authors who either failed to articulate the theoretical and methodological underpinnings of their empirical accounts of the social realm or overtly embraced holism without practical implementation. This study zeroes in on Boudon's analysis of the implicit methodology employed by Tocqueville, whom he regards as one of the pioneering individualist sociologists. Additionally, the study examines Durkheim, highlighting the paradox between his methodological claims and the actual provision of an individualist account of magic and other social phenomena. Boudon and the Cognitive Function of the Social Sciences To grasp Boundon's conception of MI, it is essential to delve into his perspective on the nature and objectives of scientific social research. According to Boudon (1993, 4), social sciences have historically served three primary functions.

2024

Telemonitoring healthcare solutions often struggle to provide the hoped for efficiency improvements in managing chronic illness because of the difficulty interpreting sensor data remotely. Computer-Mediated Social Sensemaking (CMSS) is an approach to solving this problem that leverages the patient's social network to supply the missing contextual detail so that remote doctors can make more accurate decisions. However, implementing CMSS solutions is difficult because users need to know who can see which information, and whether private and confidential information is adequately protected. In this paper, we wish to explore how socio-material design solutions might offer ways of making properties of a CMSS solution tangible to participants so that they can control and understand the implications of their participation.

2024, Springer eBooks

adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

2024, Jack M. Balkin

Tradução do artigo THE THREE LAWS OF ROBOTICS, de Jack M. Balkin, para a Revista Direitos Fundamentais e Democracia, do Programa de Pós-Graduação em Direito do Centro Universitário Autônomo do Brasil - UniBrasil

2024, IV Encontro Virtual da ABCiber

Resumo Expandido apresentado durante o ABCIBER 2024

2024

Computational approaches have grown in prominence amidst advancements in new media and technologies and ever-increasing amounts of digital data. This article critically examines these automated techniques, especially the analytical a ordances and concerns that such methods introduce to the study of online migrant and mobility discourses. The paper further argues for a mixed methodology anchored on social representations theory-a contextually sensitive framework that enables reflexive use of computational approaches, i.e., quantitatively analyze but also explore di erent layers of cultural and linguistic meanings in online diasporic interactions. With Filipino migrants in Germany as a case study and partner community, the study then demonstrates the combined application of topic modeling and ethnographically inspired qualitative analysis on migrant posts in Facebook. The findings are discussed in the form of a cultural reflection on Filipino values and expectations and an advocacy for mixed methodologies grounded on critical, social, and practice-oriented theories.

2024, Data Mining and Knowledge Discovery

Modern data-oriented applications often require integrating data from multiple heterogeneous sources. When these datasets share attributes, but are otherwise unlinked, there is no way to join them and reason at the individual level explicitly. However, as we show in this work, this does not prevent probabilistic reasoning over these heterogeneous datasets even when the data and shared attributes exhibit significant mismatches that are common in real-world data. Different datasets have different sample biases, disagree on category definitions and spatial representations, collect data at different temporal intervals, and mix aggregate-level with individual data. In this work, we demonstrate how a set of Bayesian network motifs allows all of these mismatches to be resolved in a composable framework that permits joint probabilistic reasoning over all datasets without manipulating, modifying, or imputing the original data, thus avoiding potentially harmful assumptions. We provide an open source Python tool that encapsulates our methodology and demonstrate this tool on a number of real-world use cases.

2024

Taking the impact of the Fukushima incident on the global discourse about nuclear energy as a case study, the present paper shows how to integrate computational linguistic methods into corpus-based discourse analysis (CDA). After an extensive literature review with regards to the related hermeneutic work, we present the corpus linguistic methods and point out methodological extensions. These extensions include visualization techniques that might help hermeneutic researchers explore large corpora, and second-order collocates, which help triangulate the semantics of lexical items. In our case study, we firstly give an in-depth analysis of the discourses that have formed around salient lexical items, in particular nuclear phase-out and energy transition, in the German Frankfurter Allgemeine Zeitung (FAZ) and the Japanese Yomiuri (both are conservative newspapers of the respective countries). We then provide preliminary results for the impact that the discourse had on German Twitter dat...

2024, Big data

In this article, we present results on the identification and behavioral analysis of social bots in a sample of 542,584 Tweets, collected before and after Japan's 2014 general election. Typical forms of bot activity include massive Retweeting and repeated posting of (nearly) the same message, sometimes used in combination. We focus on the second method and present (1) a case study on several patterns of bot activity, (2) methodological considerations on the automatic identification of such patterns and the prerequisite near-duplicate detection, and (3) we give qualitative insights into the purposes behind the usage of social/political bots. We argue that it was in the latency of the semi-public sphere of social media-and not in the visible or manifest public sphere (official campaign platform, mass media)-where Shinzō Abe's hidden nationalist agenda interlocked and overlapped with the one propagated by organizations such as Nippon Kaigi and Internet right-wingers (netto uyo)...

2024

2024, Social Science Computer Review

The domains of computational social anthropology and computational ethnography refer to the computational processing or computational modelling of data for anthropological or ethnographic research. In this context, the article surveys the use of computational methods regarding the production and the representation of knowledge. The ultimate goal of the study is to highlight the significance of modelling ethnographic data and anthropological knowledge by harnessing the potential of the semantic web. The first objective was to review the use of computational methods in anthropological research focusing on the last 25 years, while the second objective was to explore the potential of the semantic web focusing on existing technologies for ontological representation. For these purposes, the study explores the use of computers in anthropology regarding data processing and data modelling for more effective data processing. The survey reveals that there is an ongoing transition from the inst...

2024

O uso de robôs nas mídias sociais cresce paralelamente ao aumento da difusão de informações na internet durante eventos políticos, complicando a identificação de fontes confiáveis e a distinção entre fatos e mentiras no contexto da pós-verdade. Este livro examina a gênese e o desenvolvimento dos social bots por meio de uma revisão teórico-conceitual da sociedade pós-industrial e da era da informação. Ele contextualiza historicamente o surgimento dos social bots, os avanços tecnológicos e as transformações socioculturais do século XXI, bem como a influência da ficção científica e as recentes pesquisas sobre o tema. Além de uma análise histórica e de processos, o estudo inclui uma revisão de setenta e oito artigos científicos sobre contas automatizadas em redes sociais digitais, destacando as possibilidades de investigação e o caráter interdisciplinar dos social bots.

2024, SAGE Open

Long-standing results in urban studies have shown correlation of population and population density to a city’s pace of life, empirically tested by examining whether individuals in bigger cities walk faster, spend less time buying stamps, or make greater numbers of telephone calls. Contemporary social media presents a new opportunity to test these hypotheses. This study examines whether users of the social media platform Twitter in larger and denser American cities tweet at a faster rate than their counterparts in smaller and sparser ones. Contrary to how telephony usage and productivity scale superlinearly with city population, the total volume of tweets in cities scales sublinearly. This is similar to the economies of scale in city infrastructures like gas stations. When looking at individuals, however, greater population density is associated with faster tweeting. The discrepancy between the ecological correlation and individual behavior is resolved by noting that larger cities ha...

2024

Nos últimos anos, a influência das mídias sociais nas eleicões em
todo o mundo tornou-se cada vez mais evidente, como exemplo, o Twitter. Os textos postados no Twitter te atraído atenção significativa como uma importante fonte de informações que podem orientar muitos processos de tomada de decisão. No entanto, torna-se difícil analisar manualmente todos os comentários sobre um determinado assunto na internet. Neste contexto, o objetivo deste estudo é explorar a aplicacão da combinacão do GPT-3 e Aprendizadode Máquina para a an´alise de sentimento de postagens de usuários no Twitter durante o segundo turno das eleicões presidenciais brasileiras de 2022. Os resultados revelam que a combinação do GPT-3 e Aprendizado de M´aquina foi capaz de classificar e identificar com precis˜ao os sentimentos das postagens de usuários no Twitter. O método proposto, obteve uma acurácia de 90,88% usando o algoritmo de classificação Multinomial Naive Bayes.

2024, Journalism and Media

In this article, we propose an observational, narrowing-down approach to analysing social media networks and developing research design by the joint use of computational algorithms and researchers’ inductive exploration and interpretive explanations. The Brexit referendum on Twitter study is used to illustrate how we applied this approach in practice. In this study, observation helped us combine the strengths of computational statistical analysis and modelling and of inductive inquiries. Computational algorithms and tools including Elasticsearch, Kibana and Gephi provided us with an “ethnographic field” where we were able to inductively observe the relationships among users and to reduce the amount of data down to a level in which we could intuitively understand these relationships. In traditional observational studies, talking to human subjects and observing their interactions in a research site are important to ethnographers. Likewise, it is useful for social science researchers t...

2024, Atas da conferência Ibero-Americana WWW/Internet 2019

Uma série de robôs que realizam ataques simulados de engenharia social do tipo phishing, na plataforma twitter foi desenvolvida iterativamente. Três diferentes experimentos coletando dados para a identificação de atributos das contas que indicassem quais delas seriam mais vulneráveis a esse tipo de ataque, visando o futuro desenvolvimento de estratégias para sensibilização de usuários foram executados. Embora os resultados sobre os preditores de comportamento de usuários ainda sejam inconclusivos, os demais resultados obtidos indicam ser possível a execução continuada de ataques de phishing no twitter, apesar das estritas políticas de segurança adotadas na plataforma.

2024, Computational and Mathematical Organization Theory

The DARPA Ground Truth project sought to evaluate social science by constructing four varied simulated social worlds with hidden causality and unleashed teams of scientists to collect data, discover their causal structure, predict their future, and prescribe policies to create desired outcomes. This large-scale, long-term experiment of in silico social science, about which the ground truth of simulated worlds was known, but not by us, reveals the limits of contemporary quantitative social science methodology. First, problem solving without a shared ontology-in which many world characteristics remain existentially uncertain-poses strong limits to quantitative analysis even when scientists share a common task, and suggests how they could become insurmountable without it. Second, data labels biased the associations our analysts made and assumptions they employed, often away from the simulated causal processes those labels signified, suggesting limits on the degree to which analytic concepts developed in one domain may port to others. Third, the current standard for computational social science publication is a demonstration of novel causes, but this limits the relevance of models to solve problems and propose policies that benefit from the simpler and less surprising answers associated with most important causes, or the combination of all causes. Fourth, most singular quantitative methods applied on their own did not help to solve most analytical challenges, and we explored a range of established and emerging methods, including probabilistic programming, deep neural networks, systems of predictive probabilistic finite state machines, and more to achieve plausible solutions. However, despite these limitations common to the current practice of computational social science, we find on the positive side that even imperfect knowledge can be sufficient to identify robust prediction if a more pluralistic approach is applied. Applying competing approaches by distinct subteams, including at one point the vast TopCoder.com global community of problem solvers, enabled discovery of many aspects of the relevant structure underlying worlds that singular methods could not. Together, these lessons suggest how different a policy-oriented computational social science would be than the C. Graziul et al. 1 3 computational social science we have inherited. Computational social science that serves policy would need to endure more failure, sustain more diversity, maintain more uncertainty, and allow for more complexity than current institutions support.

2024, SSRN Electronic Journal

To which extent can data science methods-such as machine learning, text analysis, or sentiment analysis-push the research frontier in the social sciences? This essay briefly describes the most prominent data science techniques that lend themselves to analyses of institutional and organizational governance structures. We elaborate on several examples applying data science to analyze legal, political, and social institutions and sketch how specific data science techniques can be used to study important research questions that could not (to the same extent) be studied without these techniques. We conclude by comparing the main strengths and limitations of computational social science with traditional empirical research methods and its relation to theory.

2024, 2015 IEEE 11th International Conference on e-Science

In modeling social interaction online, it is important to understand when people are reacting to each other. Many systems have explicit indicators of replies, such as threading in discussion forums or replies and retweets in Twitter. However, it is likely these explicit indicators capture only part of people's reactions to each other; thus, computational social science approaches that use them to infer relationships or influence are likely to miss the mark. This paper explores the problem of detecting non-explicit responses, presenting a new approach that uses tf-idf similarity between a user's own tweets and recent tweets by people they follow. Based on a month's worth of posting data from 449 ego networks in Twitter, this method demonstrates that it is likely that at least 11% of reactions are not captured by the explicit reply and retweet mechanisms. Further, these uncaptured reactions are not evenly distributed between users: some users, who create replies and retweets without using the official interface mechanisms, are much more responsive to followees than they appear. This suggests that detecting non-explicit responses is an important consideration in mitigating biases and building more accurate models when using these markers to study social interaction and information diffusion.

2024, ACM Transactions on Interactive Intelligent Systems

Machine learning (ML) has become increasingly influential to human society, yet the primary advancements and applications of ML are driven by research in only a few computational disciplines. Even applications that affect or analyze human behaviors and social structures are often developed with limited input from experts outside of computational fields. Social scientists—experts trained to examine and explain the complexity of human behavior and interactions in the world—have considerable expertise to contribute to the development of ML applications for human-generated data, and their analytic practices could benefit from more human-centered ML methods. Although a few researchers have highlighted some gaps between ML and social sciences [51, 57, 70], most discussions only focus on quantitative methods. Yet many social science disciplines rely heavily on qualitative methods to distill patterns that are challenging to discover through quantitative data. One common analysis method for ...

2024

In this essay we make four interrelated points. First, we reiterate previous arguments (Kleinberg et al 2015) that forecasting problems are more common in social science than is often appreciated. From this observation it follows that social scientists should care about predictive accuracy in addition to unbiased or consistent estimation of causal relationships. Second, we argue that social scientists should be interested in prediction even if they have no interest in forecasting per se. Whether they do so explicitly or not, that is, causal claims necessarily make predictions; thus it is both fair and arguably useful to hold them accountable for the accuracy of the predictions they make. Third, we argue that prediction, used in either of the above two senses, is a useful metric for quantifying progress. Important differences between social science explanations and machine learning algorithms notwithstanding, social scientists can still learn from approaches like the Common Task Fram...

2024, JMIR Infodemiology

Background An infodemic is excess information, including false or misleading information, that spreads in digital and physical environments during a public health emergency. The COVID-19 pandemic has been accompanied by an unprecedented global infodemic that has led to confusion about the benefits of medical and public health interventions, with substantial impact on risk-taking and health-seeking behaviors, eroding trust in health authorities and compromising the effectiveness of public health responses and policies. Standardized measures are needed to quantify the harmful impacts of the infodemic in a systematic and methodologically robust manner, as well as harmonizing highly divergent approaches currently explored for this purpose. This can serve as a foundation for a systematic, evidence-based approach to monitoring, identifying, and mitigating future infodemic harms in emergency preparedness and prevention. Objective In this paper, we summarize the Fifth World Health Organizat...

2024, European Journal of Public Health

Background Following the World Health Organization's initial infodemic consultation in April 2020, a major infodemic conference was organised virtually in June-July 2020. Hundreds of experts participated to define science of infodemiology and build a public health research agenda that serves as a playbook for conducting relevant researches. Research Agenda provides guidance to invest in research and innovation so that we have better interventions and tools to understand, measure and respond to infodemics, and steer people towards timely, accessible, understandable information for good health choices. Methods The research agenda was developed during a virtual meeting, followed by research question prioritization exercise. It consisted of eight days spread out over four weeks. These were made up of: public preconference meeting; scientific conference, consisting of opening/closing plenary meetings either side of four separate “topic sprint” days; final public meeting to present th...

2024, Journal of Artificial Societies and Social Simulation

This article suggests to view peer review as a social interaction problem and shows reasons for social simulators to investigate it. Although essential for science, peer review is largely understudied and current attempts to reform it are not supported by scientific evidence. We suggest that there is room for social simulation to fill this gap by spotlighting social mechanisms behind peer review at the microscope and understanding their implications for the science system. In particular, social simulation could help to understand why voluntary peer review works at all, explore the relevance of social sanctions and reputational motives to increase the commitment of agents involved, cast light on the economic cost of this institution for the science system and understand the influence of signals and social networks in determining biases in the reviewing process. Finally, social simulation could help to test policy scenarios to maximise the efficacy and efficiency of various peer review schemes under specific circumstances and for everyone involved.

2024, JMIR infodemiology

Background: COVID-19 has introduced yet another opportunity to web-based sellers of loosely regulated substances, such as cannabidiol (CBD), to promote sales under false pretenses of curing the disease. Therefore, it has become necessary to innovate ways to identify such instances of misinformation. Objective: We sought to identify COVID-19 misinformation as it relates to the sales or promotion of CBD and used transformer-based language models to identify tweets semantically similar to quotes taken from known instances of misinformation. In this case, the known misinformation was the publicly available Warning Letters from Food and Drug Administration (FDA). Methods: We collected tweets using CBD-and COVID-19-related terms. Using a previously trained model, we extracted the tweets indicating commercialization and sales of CBD and annotated those containing COVID-19 misinformation according to the FDA definitions. We encoded the collection of tweets and misinformation quotes into sentence vectors and then calculated the cosine similarity between each quote and each tweet. This allowed us to establish a threshold to identify tweets that were making false claims regarding CBD and COVID-19 while minimizing the instances of false positives. Results: We demonstrated that by using quotes taken from Warning Letters issued by FDA to perpetrators of similar misinformation, we can identify semantically similar tweets that also contain misinformation. This was accomplished by identifying a cosine distance threshold between the sentence vectors of the Warning Letters and tweets. Conclusions: This research shows that commercial CBD or COVID-19 misinformation can potentially be identified and curbed using transformer-based language models and known prior instances of misinformation. Our approach functions without the need for labeled data, potentially reducing the time at which misinformation can be identified. Our approach shows promise in that it is easily adapted to identify other forms of misinformation related to loosely regulated substances.