Computational Social Science

Although artificial intelligence is the product of science-fiction literature, it currently represents a significant branch of computer science dealing with intelligent behavior, machine learning, and machine adaptation. It has become a discipline that attempts to answer real-world problems. Artificial intelligence systems are nowadays widely used in economics and medicine, design or military. The role of archives is changing worldwide. In this grandiose transformation, archives need to be at the forefront of their own future, so that they can steer, guide, and not lose out.
The vast masses of information in archives provide an excellent platform for the exploitation of artificial intelligence. The mass of data can be a great help not only for research but also for policy preparation and in some areas of public administration in the not too distant future.

We present a novel approach to computational modeling of social interactions based on modeling of essential social interaction predicates (ESIPs) such as joint attention and entrainment. Based on sound social psychological theory and methodology, we collect a new "Tower Game" dataset consisting of audio-visual capture of dyadic interactions labeled with the ESIPs. We expect this dataset to provide a new avenue for research in computational social interaction modeling. We propose a novel joint Discriminative Conditional Restricted Boltzmann Machine (DCRBM) model that combines a discriminative component with the generative power of CRBMs. Such a combination enables us to uncover actionable constituents of the ESIPs in two steps. First, we train the DCRBM model on the labeled data and get accurate (76\%-49\% across various ESIPs) detection of the predicates. Second, we exploit the generative capability of DCRBMs to activate the trained model so as to generate the lower-level ...

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.

Unequal contributions to social media, whereby a vast amount of content is produced bya limited number of users, need a better, theoretically grounded explanation. The presentpaper argues that contribution inequality on social media can be considered a symptom ofsocial differentiation, which can be detected via social entropy. A conceptual foundationfor this perspective is offered, supported by an empirical example. We use social entropy to detect the evolution of contribution inequality on Wikipedia over a period of 9 years. The paper also offers suggestions for using the conceptual framework for better understanding social media contribution structure and functional roles for practical applications.

8th International Conference on Computational Science and Engineering (CSE 2020)
focuses on Complex systems, information and computation using mathematics and
engineering techniques. This conference will act as a major forum for the presentation of
innovative ideas, approaches, developments, and research projects in the area of Computation
theory and applications. It will also serve to facilitate the exchange of information between
researchers and industry professionals to discuss the latest issues and advancement in the area
of advanced Computation and its applications will be covered during the conference

We, the people' is the most popular phrase from the constitutions. In spite of the fact that the number of countries including preamble as part of their constitution has been on the rise, preambles have received scant attention in academia. The importance of preambles has been established in multiple studies yet preambles have been looked at in isolation from socioeconomic environmental contexts. Our article attempts to present a unique insight by correlating preambles with the socio-economic, environmental, and infrastructural context within which they exist. It explores whether these correlations exist and if they do with which features and to what extent and the possibility of a causal link.

We pursue a statistical study between various indicators that reflect the growth of a country and the presence or absence of various elements in preambles across the world. Our study finds that correlations exist between the economic-social-environmental and infrastructural context of a nation-state and different elements in their preambles. Our study rigorously engages with patterns in development indicators across years to provide correlational insights into the role of preambles not just as a dormant reference but as active fragments of the socio-political-economic reality of a nation-state. We hope our article establishes grounds for a further study of the manner in which preambles and the non-political aspects of a nation-state can engage with each other.

The use of “Big Data” in policy and decision making is a current topic of debate. The 2013 murder
of Drummer Lee Rigby in Woolwich, London, UK led to an extensive public reaction on social
media, providing the opportunity to study the spread of online hate speech (cyber hate) on Twitter.
Human annotated Twitter data was collected in the immediate aftermath of Rigby’s murder to train
and test a supervised machine learning text classifier that distinguishes between hateful and/or
antagonistic responses with a focus on race, ethnicity, or religion; and more general responses.
Classification features were derived from the content of each tweet, including grammatical
dependencies  between  words  to  recognize  “othering”  phrases,  incitement  to  respond  with
antagonistic action, and claims of well-founded or justified discrimination against social groups.
The results of the classifier were optimal using a combination of probabilistic, rule-based, and
spatial-based classifiers with a voted ensemble meta-classifier. We demonstrate how the results of the
classifier can be robustly utilized in a statistical model used to forecast the likely spread of cyber
hate in a sample of Twitter data. The applications to policy and decision making are discussed

This paper argues for a virtual tech hub design ontology which would be the closest representation of the tech-innovation ecosystem. Many physical tech hubs seek to bring to one place actors and agents within the tech-innovation ecosystem. The tech-innovation ecosystem has been a primary aspect of discussion around the success or failure of many tech innovations in developing countries. Existence of an enabling ecosystem which absorbs and sustains technology innovation solutions is necessary. The question of understanding appropriate technology context as ecosystem has led to many approaches and definitions, many of which have the limitation of focusing only on a given aspect rather than the whole ecosystem of actors and their interrelations. The paper addresses and proposes a definition of the tech-innovation ecosystem, and proposes the use of the virtual tech-hub as a useful tool to measure the intensity of tech-innovation. Starting from the premise that each locale presents its own unique physical ecosystem, this paper proposes a method of representing that ecosystem in the virtual sphere through ecosystem virtualization and representation. The virtual tech hub will be the ultimate hub without limitations of space, access or tenancy. It shall allow for uninhibited engagement of innovators and ecosystem enablers and stakeholders. The virtual hub can help policy makers to correctly measure actors and their interactions, and effectively accelerate ecosystem development, through focused intervention. This paper describes a virtual hub which attempts to achieve an entire ecosystem virtualization by offering through a single window, a collaborative integrated platform.

Increasingly abundant trace data provide an opportunity for information systems researchers to generate new theory. In this research commentary, we draw on the largely “manual” tradition of the grounded theory methodology and the highly “automated” process of computational theory discovery in the sciences to develop a general approach to computationally intensive theory development from trace data. This approach involves the iterative application of four general processes: sampling, synchronic analysis, lexical framing, and diachronic analysis. We provide examples from recent research in information systems.

The Social News site reddit.com is composed of thousands of independent user-created subreddits where people use the site's submission and voting features in a variety of ways. Published research concerning reddit tends to focus on a small number of subreddits or treat the site as a whole, ignoring differences between subreddits. This paper offers an overview of all the subreddits which have been created on the site. The great majority of these subreddits are seldom used, 98% have fewer than 1,000 submitted posts, together accounting for just 1% of voting activity and 2% of commenting activity on the site. The remaining subreddits were clustered into three types, 1) those dominated by voting activity, including most of the large default subreddits, 2) a small number of subreddits which have many post submissions but few votes or comments, 3) discussion-oriented subreddits which see similar levels of commenting and voting activity. The paper concludes by considering a selection of 35 large subreddits which together account for 74% of all voting activity on the site. Differences between subreddits catering to user-generated content and 'external' content are observed. Activity rates on a subreddit's New page influence its behavior.

U.S. support for marijuana legalization grew from 38% to 65% in 2008-2019. To find the discourse features that preceded and followed the shift, I curated a comprehensive corpus of Reddit comments from the same period. Neural networks trained on human annotations of attitude and persuasion attempts separated strategic use of narratives from non-argumentative discourse. Two narrative frames considered important to persuasion in past research were studied: anecdotal vs. generalized content. I operationalized anecdotal frames based on three linguistic clause-level features: Whether the clause is about a generic kind, if it represents a reliable state or an event, and whether any events are bounded in time. A corpus of Reddit and news was annotated for these features and more, neural networks based on which estimated anecdotal properties in the broader Reddit dataset. Anecdotal themes were less prevalent but present in most comments, particularly in arguments favoring legalization. Nationally, a surge in anecdotes within non-argumentative discourse happened over time as a consequence of attitude shifts. Generalized discourse was a potential cause with major surges around the 2012 and 2016 legal milestones. Attempts to associate generalized discourse with legal changes were complicated by marijuana’s varied status across the U.S. I therefore inferred user locations and compared the rate of anecdotal themes before and after legalization in comments from pioneering states. More generalized frames set the stage for each successful legalization bid. The particular content, however, varied between the two milestones. Character judgments were prominent in 2012, while crimes and politics took center-stage in 2016. The generalized precedents of legalization in the two periods shared argumentative and moralistic focus but had distinctive clause-level profiles. Meanwhile, legal and medical arguments were sidelined, meaning the novel consensus was not informed by much of the relevant information. Together, my findings present generalized argument framing as a harbinger of attitude shift toward hot-button topics, and anecdotal non-argumentative framing as a consequence of it. The machine learning pipeline that made this insight possible is novel for social media research but general-purpose, allowing similar abstract narrative frames to be broken down into theory-driven constituents, and studied in quantitative detail.

This paper reveals the dangers of the Moral Machine experiment, alerting against both its uses for normative ends, and the whole approach it is built upon to address ethical issues. It explores additional methodological limits of the experiment on top of those already identified by its authors and provides reasons why it is inadequate in supporting ethical and juridical discussions to determine the moral settings for autonomous vehicles. Demonstrating the inner fallacy behind computational social choice methods when applied to ethical decision-making, it also warns against the dangers of computational moral systems, such as the ‘voting-based system’ recently developed out of the Moral Machine’s data. Finally, it discusses the Moral Machine’s ambiguous impact on public opinion; on the one hand, laudable for having successfully raised global awareness with regard to ethical concerns about autonomous vehicles, and on the other hand pernicious, as it has led to a significant narrowing of the spectrum of autonomous vehicle ethics, de facto imposing a strong unidirectional approach, while brushing aside other major moral issues.

https://www.tandfonline.com/doi/abs/10.1080/17579961.2021.1898310

As social media technologies such as Twitter, Instagram, and YouTube have become highly ubiquitous, social life itself has become reconfigured. Though early notions of an offline/online binary remain in some quarters of social research, there is a realization amongst most that this binary is reified. As such, the study of social interactions within social media is a fundamental sociological question. This chapter argues that social researchers need to engage with the study of social media in order to comprehensively understand modern social life. This chapter also provides insights into how we, as social researchers, can critically collect and discern social formations via social media. Twitter is specifically used in this chapter to provide an example of how the medium provides opportunities for mixed qualitative and quantitative social analysis. Ultimately, this chapter also argues that the understanding of large social questions is increasingly contingent on us deciphering and understanding social knowledge formed and maintained within social media platforms.

metaknowledge is a full-featured Python package for computational research in information science, network analysis, and science of science. It is optimized to scale efficiently for analyzing very large datasets, and is designed to integrate well with reproducible and open research workflows. It currently accepts raw data from the Web of Science, Scopus, PubMed, ProQuest Dissertations and Theses, and select funding agencies. It processes these raw data inputs and outputs a variety of datasets for quantitative analysis, including time series methods, Standard and Multi Reference Publication Year Spectroscopy, computational text analysis (e.g. topic modeling, burst analysis), and network analysis (including multi-mode, multi-level, and longitudinal networks). This article motivates the use of metaknowledge and explains its design and core functionality.

In the last 15 years different aspects of metadata quality have been investigated. Researchers measured the established metrics on a variety of metadata collections. One common aspect of the majority of these research projects is that the tools they produce as a necessary side effect were not intended to be reused in other projects. This research, while focusing mainly on a specific metadata collection, Europeana, investigates practical aspects of metadata quality measurement such as reusability, reproducability, scalability and adaptability.

In a digital age, researchers have access to large-scale data describing complex social systems. This seminar offers an introduction to tools and techniques used to obtain and analyze such data. The course examines computational social science methods and their applications to the study of communication, information, and human behavior. Theoretical discussion is combined with hands-on training using the R programming language.

How do blockbusters dominate competition in entertainment platforms? Traditionally, entertainment industries incur in high market concentration and extreme uncertainty on product sales. Today, with the disruption of the platform revolution, they face an additional level of complexity with the typical dynamics of multi-sided organizations. The video game industry is one of the oldest digital entertainment platform markets and, despite high risks, it generated US$137.9 billion in revenue with a consumer base of more than 2 billion gamers in 2017. I take the home console market as a case study to study hit-making emergence in entertainment platform markets. I present how publishers market concentration appears within platform's 'winner-takes-most' competition. After an empirical survey on the market's history and actors behavior, I present an agent-based model that surveys platform's life-cycle on the home console sector of North America. I find peer influence in a multi-sided organization is sufficient to reproduce the industry's typical life-cycle, high market concentration, and extreme uncertainty.

In the last decade, the investigation of the social complexity has witnessed the rise of Computational Social Science, a research paradigm that heavily relies upon data and computation to foster our understanding of social phenomena. In this field, a key role is played by the explanatory and predictive power of agent-based social simulations that are showing to take advantage of GIS, higher number of agents and real data. We focus GIS based distibuted ABMs. We observed that the density distribution of agents, over the field, strongly impact on the overall performances. In order to better understand this issue, we analyzes three different scenarios ranging from real positioning, where the citizens are positioned according to a real dataset to a random positioning where the agent are positioned uniformly at random on the field. Results confirm our hypothesis and show that an irregular distribution of the agents over the field increases the communication overhead. We provide also an analytic analysis which, in a 2-dimensional uniform field partitioning, is affected by several parameters (which depend on the model), but is also influenced by the density distribution of agents over the field. According to the presented results, we have that uniform space partitioning strategy does not scale on GIS based ABM characterized by an irregular distribution of agents.

Online web services such as Google and Facebook started using personalization algorithms. Because information is customized per user by the algorithms of these services, two users who use the same search query or have the same friend list may get different results. Online services argue that by using personalization algorithms, they may show the most relevant information for each user, hence increasing user satisfaction. However, critics argue that the opaque filters used by online services will only show agreeable political viewpoints to the users and the users never get challenged by opposing perspectives. Considering users are already biased in seeking like-minded perspectives, viewpoint diversity will diminish and the users may get trapped in a “filter bubble”. This is an undesired behavior for almost all democracy models.

In this thesis we first analyzed the filter bubble phenomenon conceptually, by identifying internal processes and factors in online web services that might cause filter bubbles. Later, we analyzed this issue empirically. We first studied existing metrics in viewpoint diversity research of the computer science literature. We also extended these metrics by adding a new one, namely minority access from media and communication studies.  After conducting an empirical study for Dutch and Turkish Twitter users, we showed that minorities cannot reach a large percentage of users in Turkish Twittersphere. We also analyzed software tools and design attempts to combat filter bubbles. We showed that almost all of the tools implement norms required by two popular democracy models. We argue that democracy is essentially a contested concept, and other less popular democracy models should be included in the design of such tools as well.

Course Description: This course provides an applied, non-technical introduction to the methods and ideas of Computational Social Sciences. It discusses how new online data sources and the computational methods shed new light on old social science questions and ask brand new questions. It articulates on some of the ethical and privacy challenges of living in a world of big data and algorithmic decision making.

In light of the exponential growth in digital data characterizing the 21st century, future historians of our time will have to rely on born-digital materials as primary sources for establishing historical facts. Yet born-digital materials challenge historians’ well-established source criticism techniques used for establishing facts based on the authenticity, authorship and authority of documents, for they are ephemeral, immaterial, fragile and easy to manipulate. For example, the content of websites can be easily modified, tweets are frequently deleted, the number of social media comments and likes can be artificially boosted through click farms, and dubious sources spreading misinformation can be disguised as reliable news organizations. With the commercialization of the web, more than ever before, web data is primarily proprietary, and therefore subjected to platforms’ policies and constraints.

The International Journal of Information Science & Techniques (IJIST) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.

This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.

Reddit's men's rights community (/r/MensRights) has been criticized for the promotion of misogynistic language, toxic masculinity and discourses that reinforce alt-right ideologies. Conversely, the men's liberation (/r/MensLib) community integrates inclusive politics, intersectionality and masculinity within a broad umbrella of self-reflection that suggests toxic masculinity harms men as well as women. We use machine learning text classifiers, keyword frequencies , and qualitative approaches first to distinguish these two subreddits, and second to interpret the differences ideologically rather than topically. We further integrate platform metadata (referred to as 'platform signals') to distinguish the subreddits. These signals help us understand how similar terms can be used to arrive at different interpretations of gender and discrimination. Where /r/MensLib tends to see masculinity as an adjective and women as peers, /r/MensRights views being a man as an essential quality, men as the target of discrimination, and women as sources of personalized grievances.

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.

From September 4 to 11, 1992, a first meeting between Ukrainian and German scientists interested in mathematical and computer modeling of social processes was held at Vorzel' near Kiev. The meeting had been planned for nearly three years by Igor V. Chernenko and Mikhail V. Kuz'min, then members of the research group on mathematical modeling in sociology at the Institute of Sociology of the Academy of Science of the Ukrainian Republic, and had to be postponed twice due to the political development in the former Soviet Union, but thanks to the organizers' perseverance (and in spite of a strike of the airport personell at Kiev Borispol Airport on the eve of the conference) the conference could at last be realized.

The main purpose of the conference was to discuss a synergetic interpretation of large-scale destructive social processes as catastrophic phenomena in self-organized systems.

Collaboration between German and Ukrainian scientists in the field of
mathematical simulation of socio-economic processes has a good tradition that gave a set of original nonlinear models of creative self-organization as well as a new catastrophic approach to examine social evolution. The conditional forecasts of social cataclysms obtained in the papers presented at the conference were corroborated by the subsequent events in the former Soviet Republics.

Social catastrophe means that the self-organized driver of  system growth  is transmuted into  a factor  of  system  decay. Nonlinear models  of  such  crucial  metamorphoses  were  the  most exciting subjects of discussions,  because  these  models  provide an opportunity to see into the  contradictory  nature  of  living systems and to study hidden functional links  between  micro and macro levels of self-reproductive systems  and  conditions  of their transformations.

From the beginning of the conference preparations it had been clear that the ``chaotic'' and sometimes ``catastrophic'' processes in state and society of the successors to the Soviet republics should be in the center of interest of the participants. One of the crucial questions was whether mathematical analysis and computer simulation can make a useful contribution to the understanding of the processes of rapid social and economic change in the Community of Independent States, and whether reasonable use can be made of the precise mathematical concepts of ``chaos'' and ``catastrophe'' to describe these processes --- since the everyday meanings of these words are so often used for such a description.

These proceedings try to answer this question, starting from four papers which are intended to give some orientation in the field of modeling complex systems: Kisil gives a somewhat pessimistic view on sociological measurement as a fundamental for sociological modeling and simulation, while Weidlich and Troitzsch present the modeling approaches they have been using for some time to show that even in systems of small complexity processes of self-organization may arise. Shatikhin finishes this first part with a paper concentrating on the macroscopic view of systems.

The second part of these proceedings presents some modeling tools and
algorithms. We start with two papers on the MIMOSE modeling and simulation system which was developed by Möhring's and Troitzsch's working group at Koblenz. Borodyanski's and Burgin's papers open
a new perspective on non-algorithmic procedures. The second part is
finished by Polumiyenko's paper on a very complex game theoretical model of an ecosystem which serves as a means to show the possible contribution of game theory as a tool for understanding complex systems.

The third part contains a number of special models of self-organizing
processes, of catastrophic and of chaotic phenomena. Flache and his coauthors discuss a model of cooperation in a group rewarded team, thus focussing the microscopic perspective. The papers by Tsvetkov, Dubrovskiy, Chernenko, Chernyshenko, Kuzmin, and Platon describe several variations of a model of evolution in a society consisting of several
cooperating and competing subpopulations (a model which is also used to illustrate the facilities of the MIMOSE simulation system in Möhring's and Strotmann's paper). While this model and the variations described in these three papers is macroscopic, Troitzsch's paper investigates the catastrophic jump from a microscopic perspective. Chesnokov and Chernenko in their paper try to reconcile concepts of Eastern mystic and modern mathematics to understand self-organization processes. Niyazov gives a semi-empirical analysis of large cycles in US economy during the last two centuries and proposes a model which could make these cycles understandable.

The proceedings end in a paper presented by Erdmann and discussed for hours by the conference participants, in which he models economic aspects of institutional change in Eastern Europe, proposing some political recommendations how to improve the economical situation in the countries of Eastern Europe.

Automatic routines are the most difficult to change. However, they play a key role in the way individuals use electricity in their homes. Recognizing the impact of routines in electricity usage decisions, the research work presented in this thesis, focuses on the discovery of patterns of daily electricity in households. The research is based upon the data collected at a small-scale experiment, which resembles a utility automated meter infrastructure (AMI) deployment. Computer software provided historical and real-time information to individuals. The data visualization and transmission architecture relied on the home area network of the households.
The fundamental contributions of the present research are the following: a) a methodology that recognizes daily patterns of consumption, b) a methodology that generates typical load curves that are specific of a combination of external conditions such as outdoor weather temperature, working day / weekend and month of the year. Both methodologies should be tested with larger samples, but may define a building block to improve consumer driven programs based upon segmentation by actual electricity consumption behaviors and personalized feedback.
Other contributions include verifying the circumstances in which routine patterns are more probable, which specifically include the presence of young children, and how these tend to be less predictable when children grow older. Previous research recognizes that feedback is an essential component of learning. However, insights from qualitative interviews show that learning is an incremental process and it is possible that individuals don’t relate the cause and the effect when discussing how their practices have changed across the duration of the experiment.

Agent-based computational economics (ACE) has been used for tackling major research questions in macroeconomics for at least two decades. This growing field positions itself as an alternative to dynamic stochastic general equilibrium (DSGE) models. In this paper we first review the arguments raised against DSGE in the ACE literature. We then review existing ACE models, and their empirical performance. We then turn to a literature on behavioural New Keynesian models that attempts to synthesise these two approaches to macroeconomic modelling by incorporating some of the insights of ACE into DSGE modelling. We highlight the individually rational New Keynesian model following Deak et al. (2015) and discuss how this line of research can progress.