Computational Sociology

Social relationships such as friendship and partner choice are ruled by the proximity principle, which states that the more similar two individuals are, the more likely they will become friends. However, proximity, similarity, and friendship are concepts with blurred edges and imprecise grades of membership. This study shows how to simulate these friendship dynamics in an agent-based model that applies fuzzy sets theory to implement agent attributes, rules, and social relationships, explaining the process in detail. Although in principle it may be thought that the use of fuzzy sets theory makes agent-based modelling more elaborated, in practice it saves the modeller from taking some arbitrary decisions on how to use crisp values for representing properties that are inherently fuzzy. The consequences of applying fuzzy sets and operations to define a fuzzy friendship relationship are compared with a simpler implementation, with crisp values. By integrating agent computational models and fuzzy set theory, this paper provides useful insights into scholars and practitioners to tackle the uncertainty inherent to social relationships in a systematic way.

This paper proposes a computer simulation framework for investigating the role of built form – specifically physical venues-in the dynamics of neighborhood integration and segregation. Looking to Schelling's classic work (1971) and the subsequent literature, we note, however, that with few exceptions 'form' in these models is reduced to an agent's capacity to 'see' nearby grid-cells of the simulation world: an agent's neighbourhood is its Moore neighbourhood. In this research, we argue that an analytically meaningful simulation of neighbourhood formation – or more specifically of integration and segregation dynamics – must acknowledge the role of built form. To do so, we introduce physical venues into the classic Schelling model in order to reconsider the simulation's dynamics as influenced by both the spaces where agents live and the spaces of their activities. Our analysis proceeds through a series of four case studies of increasing sophistication, considering the impact of different spatial configurations as well as different kinds of venue. We articulate a simple but powerful generative model of urban social formations that also may offer insight into the conditions under which sedimented patterns of segregation may become unsettled.

How do social ties in online worlds evolve over time?
This research examined the dynamic processes of
relationship formation, maintenance, and demise in a
massively multiplayer online game. Drawing from evolutionary
and ecological theories of social networks, this
study focuses on the impact of three sets of evolutionary
factors in the context of social relationships in the
online game EverQuest II (EQII): the aging and maturation
processes, social architecture of the game, and
homophily and proximity. A longitudinal analysis of tie
persistence and decay demonstrated the transient
nature of social relationships in EQII, but ties became
considerably more durable over time. Also, character
level similarity, shared guild membership, and geographic
proximity were powerful mechanisms in preserving
social relationships.

We introduce a conceptual-mathematical model that simulates the spread of information. We represent the states of the actors in the information flow using compartments. The compartment diagram is translated into a system of coupled ordinary differential equations. The diversity of social groups (subpopulations) is incorporated in the model, and the model is applicable for a heterogeneous community. We determine four possible spots that can be influenced to control the information propagation such as (1) controlling the inflow and outflow of people in each subpopulation, (2) regulating the media of communication, (3) influencing the belief system of the actors, and (4) introducing an antithesis to the circulating information.

The development of the web has increased the diversity of pornographic content, and at the same time the rise of online platforms has initiated a new trend of quantitative research that makes possible the analysis of data on an unpreced- ented scale. This paper explores the application of a quantitative approach to publicly available data collected from pornographic websites. Several analyses are applied to these digital traces with a focus on keywords describing videos and their underlying categorization systems. The analysis of a large network of tags shows that the accumulation of categories does not separate scripts from each other, but instead draws a multitude of significant paths between fuzzy categories. The datasets and tools we describe have been made publicly available for further study.

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 automatizados – conhecidos 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.

The urban policy mobility literature describes the widespread circulation of policy ideas while highlighting their mutations along the way. At the same time, the literature often analyzes the localization of such ideas by examining their adoption in one or several cities. To  better understand policy replications and mutations, we develop theoretical and methodological strategies that provide sensitivity to both local distinctiveness and global variability. We build on the Urban Policy Mobility literature and combine it with ecological theories of conceptual spaces to develop the concept of Urban Model Spaces –  a matrix of discursive possibilities evolving from the accumulated replications and localizations of a model. We articulate it via three core  properties central to Urban Policy Mobility - Temporality, Scale, and Position and test how they shape the emergence of  policy discourses. To demonstrate the concept we analyze public art  policy and the funding mechanism of the Percent for Art ordinance from 26 cities combining Structural Topic Modeling and regression analysis

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 Shinz o Abe's hidden nationalist agenda interlocked and overlapped with the one propagated by organizations such as Nippon Kaigi and Internet right-wingers (netto uyo) during the election campaign, the latter potentially forming an enormous online support army of Abe's agenda.

Recent methodological debates in sociology have focused on how data and analyses might be made more open and accessible, how the process of theo- rizing and knowledge production might be made more explicit, and how developing means of visualization can help address these issues. In ethnogra- phy, where scholars from various traditions do not necessarily share basic epistemological assumptions about the research enterprise with either their quantitative colleagues or one another, these issues are particularly complex. Nevertheless, ethnographers working within the field of sociology face a set of common pragmatic challenges related to managing, analyzing, and pre- senting the rich context-dependent data generated during fieldwork. Inspired by both ongoing discussions about how sociological research might be made more transparent, as well as innovations in other data-centered fields, the authors developed an interactive visual approach that provides tools for addressing these shared pragmatic challenges. They label the approach “ethnoarray” analysis. This article introduces this approach and explains how it can help scholars address widely shared logistical and technical complexities, while remaining sensitive to both ethnography’s epistemic diversity and its practitioners shared commitment to depth, context, and interpretation. The authors use data from an ethnographic study of serious illness to construct a model of an ethnoarray and explain how such an array might be linked to data repositories to facilitate new forms of analysis, inter- pretation, and sharing within scholarly and lay communities. They conclude by discussing some potential implications of the ethnoarray and related approaches for the scope, practice, and forms of ethnography.

This paper provides an overview on the impact of agent-based models in the social sciences. It focuses on the reasons why agest-based models are seen as important innovations in the recent decades. It is aimed to evaluate the impact of this innovation on various disciplines, such as economics, sociology, anthropology, and behavioural sciences. It discusses the advances it contributed to achieve and illustrates some comparatively new fields to which it gave rise. Finally, it emphasizes some research issues that need to be addressed in the future.

This note discusses two challenges to simulating the social process of science. The first is developing an adequately rich representation of the underlying Data Generation Process which scientific progress can "learn". The second is how to get effective data on what, in broad terms, the properties of the "future" are. Paradoxically, with due care, we may learn a lot about the future by studying the past.

Computational sociology models social phenomena using the concepts of emergence and downward causation. However, the theoretical status of these concepts is ambiguous; they suppose too much ontology and are invoked by two opposed sociological interpretations of social reality: the individualistic and the holistic. This paper aims to clarify those concepts and argue in favour of their heuristic value for social simulation. It does so by proposing a link between the concept of emergence and Luhmann's theory of communication. For Luhmann, society emerges from the bottom-up as communication and he describes the process by which society limits the possible selections of individuals as downward causation. It is argued that this theory is well positioned to overcome some epistemological drawbacks in computational sociology.

Introduction Voluntary organisations provide essential
support to vulnerable populations and front-line
health
responders to the COVID-19 pandemic. The French
Red Cross (FRC) is prominent among organisations
offering health and support services in the current crisis.
Comprised primarily of lay volunteers and some trained
health workers, FRC volunteers in the Paris (France) region
have faced challenges in adapting to pandemic conditions,
working with sick and vulnerable populations, managing
limited resources and coping with high demand for their
services. Existing studies of volunteers focus on individual,
social and organisational determinants of motivation, but
attend less to contextual ones. Public health incertitude
about the COVID-19 pandemic is an important feature of
this pandemic. Whether and how uncertainty interacts with
volunteer understandings and experiences of their work
and organisational relations to contribute to Red Cross
worker motivation is the focus of this investigation.
Methods and analysis This mixed-methods
study will
investigate volunteer motivation using ethnographic
methods and social network listening. Semi-structured
interviews and observations will illuminate FRC volunteer
work relations, experiences and concerns during the
pandemic. A questionnaire targeting a sample of Paris
region volunteers will allow quantification of motivation.
These findings will iteratively shape and be influenced
by a social media (Twitter) analysis of biomedical and
public health uncertainties and debates around COVID-19.
These tweets provide insight into a French lay public’s
interpretations of these debates. We evaluate whether and
how socio-political
conditions and discourses concerning
COVID-19 interact with volunteer experiences, working
conditions and organisational relations to influence
volunteer motivation. Data collection began on 15 June
2020 and will continue until 15 April 2021.
Ethics and dissemination The protocol has received
ethical approval from the Institut Pasteur Institutional
Review Board (no 2020-03). We will disseminate findings
through peer-reviewed
articles, conference presentations
and recommendations to the FRC.

Is it possible use algorithms to find trends in the history of popular music? And is it possible to predict the characteristics of future music genres?
In order to answer these questions, we produced a hand-crafted dataset with the intent to put together features about style, psychology, sociology and typology, annotated by music genre and indexed by time and decade. We collected a list of popular genres by decade from Wikipedia and scored music genres based on Wikipedia descriptions. Using statistical and machine learning techniques, we find trends in the musical preferences and use time series forecasting to evaluate the prediction of future music genres.

In the first part of this essay (Perspectives, Fall
2015), I suggested things are looking pretty
good for sociological theory, an optimism
grounded in my appreciation of emergent
sociological sub-fields where interesting
theoretical work is being paired with
innovative new measurement regimes to
create different kinds of sociological insights. I
pointed to the field of computational sociology
(or Big Data social science) as an example. In
this second part, I offer a few reasons why I
think this area of research will continue to
need more and better theory in the years
ahead. I highlight three causes, what I call: (1)
the paradigm effect, (2) the data effect, and (3)
the culture effect.

Social relationships such as friendship and partner choice are ruled by theproximity principle, which states that the more similar two individuals are, the more likely they will become friends. However, proximity, similarity, and friendship are concepts with blurred edges and imprecise grades of membership. This study shows how to simulate these friendship dynamics in an agent-based model that appliesfuzzy sets theoryto implement agent attributes, rules, and social relationships, explaining the process in detail. Although in principle it may be thought that the use of fuzzy sets theory makes agent-based modelling more elaborated, in practice it saves the modeller from taking some arbitrary decisions on how to use crisp values for representing properties that are inherently fuzzy. The consequences of applying fuzzy sets and operations to define a fuzzy friendship relationship are compared with a simpler implementation, with crisp values. By integrating agent computational models and ...

In a groundbreaking article, Moody and White (2003) introduced the concept of structural cohesion, simultaneously characterizing emergent communities and their internally embedded layers by the number of node-independent paths interconnecting individuals. Like many studies, however, they “corrected” the directionality discovered in some of their data. While often done for important purposes, doing so potentially confounds structural cohesion with unrelated concepts. Some relations, especially those relating to the dynamic aspects of social life, are inherently directed, in whole or in part, and it may prove worthwhile to respect this directionality. In this article, I recast structural cohesion in terms of directed social relations and identify four distinct ways of measuring it. In two example data sets—hiring relations among graduate programs and trust relations among neighborhood residents—I show that only strong embeddedness, a type of structural cohesion emerging from directed relations, proves to be a powerful, robust, independent explanatory factor. I further show that if the directionality in the data in these examples had been “corrected,” the importance of structural cohesion would have been dramatically undervalued.

The urban policy mobility literature describes the widespread circulation of policy ideas while highlighting their mutations along the way. At the same time, the literature often analyzes the localization of such ideas by examining their adoption in one or several cities. To better understand policy replications and mutations, we develop theoretical and methodological strategies that provide sensitivity to both local distinctiveness and global variability. We build on the Urban Policy Mobility literature and combine it with ecological theories of conceptual spaces to develop the concept of Urban Model Spaces – a matrix of discursive possibilities evolving from the accumulated replications and localizations of a model. We articulate it via three core properties central to Urban Policy Mobility - Temporality, Scale, and Position and test how they shape the emergence of policy discourses. To demonstrate the concept we analyze public art policy and the funding mechanism of the Percent for Art ordinance from 26 cities combining Structural Topic Modeling and regression analysis.

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 have sublinear
growth in the number of active Twitter users. This suggests that there is a more concentrated core of more active users that
may serve an information broadcast function for larger cities, an emerging group of “town tweeters” as it were.

In some ways, the balancing of theory and practice is always needed, but that need is also, I think, more pronounced in some places and at some times than others. My sense is that the embrace of edgy theory-infused empirical work is on the upswing in sociology, and I think this bodes well for sociological theory. I say this in part because I have been watching a number of new research programs coming into formation where scholars are finding the headroom to bring broad, smart, and interesting theorizing to bear on problems that are deeply embedded in, and indeed constitutive of, the design and conceptualization of the empirical work itself. Often, this research is more exploratory than confirmatory, and, as I will try to explain here with regard to the case of the new field of computational sociology, I think we can identify some of the reasons why this is happening more frequently now than before.

How does Google predict flu peaks before public health authorities do? Why are these predictions wrong at times? How did Walmart link a spike in the sales of Pop-Tarts to hurricane forecasts? Why are people's inadvertent little gestures more revealing of their authentic character than any formal posture they may carefully construct? How can a single piece of unintentional information be infinitely more informative than thousands of public records? What kind of data does Netflix use to profile its customers? What did the historian Marc Bloch mean by «witnesses in spite of themselves»?

In the attempt to provide scientifically sound answers and stimulate analytically innovative discussion, the conference focuses on a particular but significant theme in the social sciences: the concepts of "footprint" and "trace". Usually associated with the digital world, the very idea of footprints clearly gives the image of what one leaves behind without being aware of it. A sort of indentation, a by-product of other (differently focused and oriented) actions that remains empirically imprinted on reality-a footstep, an impression, an epiphenomenon. Under this light, the difference between information that is intentionally communicated (given) and information that is accidentally exuded (given off) seems to be a profound one, which identifies two deeply different kinds of communicative content (that is, signs and signals). Trace-like information, i.e. information that was not meant to be informative, is much sought after, and this is particularly true in the age of digital capitalism. The opportunity to interpret those trails that people leave behind themselves,  brushing social reality «against the grain» (i.e. against the reasons and intentions of the people involved, to
borrow Walter Benjamin’s expression), opens new scenarios for social theory and applied social research: what are the stakes? What are the risks? What is the potential of this approach?

Given the multifaceted objectives involved in exploring these issues, the conference intends to bring together heuristic aspects and ethical dimensions, scientific analysis and political considerations, empirical perspectives and theoretical foundation. At the same time, the main purpose is to hold together cultural analysis and social theory, communication and Internet studies, big-data informed research and computational social science. We encourage inter- and cross-disciplinary thinking and research to address all the issues relevant to the general theme. Contributions from sociology, communication studies, data science, anthropology, computational social science, history, semiotics, media and Internet studies – among others – are welcome.

This article presents Networked Pantheon, a relational database of biographies of globally famous people spanning the last 5,500 years of human history. This source of information is intended to complement Pantheon 1.0 (Yu et. al. 2016), a biographical dataset that includes temporal, spatial, gender, and occupational information on 11,341 world-renowned people-defined as those who have their biographies in 25 or more Wikipedia language-versions-. Networked Pantheon adds information about the biographical links between these historical figures, which was compiled from the hyperlinks between the biographies in English Wikipedia. This digital method allows technics from network analysis to be used to study the relationships between globally famous people, and thus to calculate different measures of historical centrality for individuals, cities, countries, genders, and occupations. Networked Pantheon complements the historical popularity indicators of Pantheon 1.0 with measurements of the centrality of the figures in the network of biographical references, allowing for an approximation to the information flows between different territories, genders, and occupations of famous people over time.

This paper investigates the relevance of reputation to improve the explorative capabilities of agents in uncertain environments. We have presented a laboratory experiment where sixty-four subjects were asked to take iterated economic investment decisions. An agent-based model based on their behavioural patterns replicated the experiment exactly. Exploring this experimentally grounded model, we studied the effects of various reputational mechanisms on explorative capabilities at a systemic level. The results showed that reputation mechanisms increase the agents' capability for coping with uncertain environments more than individualistic atomistic exploration strategies, although the former does entail a certain amount of false information inside the system.

ABSTRACT This article demonstrates how a technique called Agent-Based Modelling can address a significant challenge for effective interdisciplinarity. Different disciplines and research methods make divergent assertions about what a satisfactory explanation requires. However, without a unified framework analysing the implications of these differences systematically, debate cannot transcend mere competing assertion. Using a sequence of examples, I demonstrate that Agent-Based Modelling provides such a unified framework by showing how pairs of models may display quantitatively distinct behaviours when differing only in (for example) including a social network. The ability to quantify differences arising from divergent assumptions about what models should include makes them subject to empirical evaluation (rather than mere contention). Although the article uses social network examples for accessible presentation, the approach of building paired models is quite general and can, therefore, illuminate other significant social science controversies (like the role of rationality and the importance of ethnographic detail).