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Social scientists of class and inequality have documented the rise of omnivorousness, informality, ordinariness, and emphasis on meritocracy. This apparent decline in class closure contrasts sharply with rising inequality and declining economic mobility. How are these competing developments reflected in everyday class distinction-making? In this article, we answer this question by applying Goffman's work on the symbols of class status to the analysis of unique data: a corpus of etiquette books published between 1922 and 2017. We use word embeddings to quantify the salience of six class concepts (affluence, cultivation, education, employment, morality, and status) in the corpus. We find that education and employment are increasingly salient while status, affluence, cultivation, and morality decline in their salience to class distinction-making. These results signal a decline of class operating as a status group through cultural closure, the rise of education and employment as the carriers of class in everyday life, and the corresponding legitimation of class position and class inequality based on supposedly meritocratic grounds. This research opens up new avenues for studies of class and the application of computational methods to investigations of social change.

Unbiased science dissemination has the potential to alleviate some of the known gender disparities in academia by exposing female scholars’ work to other scientists and the public. And yet, we lack comprehensive understanding of the relationship between gender and science dissemination online. Our large-scale analyses, encompassing half a million scholars, revealed that female scholars’ work is mentioned less frequently than male scholars’ work in all research areas. When exploring the characteristics associated with online success, we found that the impact of prior work, social capital, and gendered tie formation in coauthorship networks are linked with online success for men, but not for women—even in the areas with the highest female representation. These results suggest that while men’s scientific impact and collaboration networks are associated with higher visibility online, there are no universally identifiable facets associated with success for women. Our comprehensive empirical evidence indicates that the gender gap in online science dissemination is coupled with a lack of understanding the characteristics that are linked with female scholars’ success, which might hinder efforts to close the gender gap in visibility.

The rapid technological evolution of the last years motivated students to develop competencies and capabilities that will prepare them for an unknown future of the 21st century. In this context, teachers intend to optimise the process of learning and make it more dynamic and exciting by introducing gamification. Thus, this paper focuses on a data-driven assessment of geometry competencies, which are essential for developing problem-solving and higher-order thinking skills. We explored them in the domain of knowledge inference, whose primary goal is to predict or measure the students' knowledge over questions as they interact with a learning platform at a specific time. Hence, the main goal of the current paper is to compare several well-known algorithms applied to the data of a geometry game named Shadowspect in order to predict students' performance in terms of classifier metrics such as Area Under Curve (AUC), accuracy, and F1 score. We found Elo to be the algorithm with the best prediction power. However, the rest of the algorithms also showed decent results, and, therefore, we can conclude that all the algorithms hold the potential to measure and estimate the actual knowledge of students. In turn, this means that they can be applied in formal education to improve teaching, learning, organisational efficiency and, as a consequence, this can serve as a basement for a change in the system.

Who writes the news in the Chilean press according to gender? Who are the sources, male or female, in the Chilean press? Is there a relationship between the gender of journalists and the gender of the sources in the Chilean press? This article studies the gender of the Chilean newsroom and their sources in 12,113 news through a quantitative method with a computational social science approach. This method combines web scraping and natural language processing techniques to gather and preprocess data, facilitating the exploration of complex social phenomena. Results show important biases in journalists and source gender. From a sample of 158 journalists, 99 were men (63%) and 59 women (37%). Also, from 12,113 news, 7,565 (62%) were written by male and 4,548 (38%) by female journalists. Of the 12,334 sources mentioned in the news, 9,771 were men (79%) and 2,563 were women (21%). A significant finding is that equality in the newsroom is related to how female and male journalists choose their sources. In other words, when a media has a newsroom with gender equality, the sources of the journalists are more equitable too. These results have important insights to discuss within the journalism schools, to make students aware of the gender bias in the profession. Furthermore, if the presence of female – journalists or sources- increased in the media, it would allow them to grow their media power and status. ¿Quién escribe las noticias en la prensa chilena según el género? ¿Las fuentes son masculinas o femeninas en la prensa chilena? ¿Existe una relación entre el género de los y las periodistas y el género de las fuentes en la prensa chilena? Este artículo estudia el género de los y las periodistas y sus fuentes en 12.113 noticias, a través de un método cuantitativo con enfoque de ciencias sociales computacionales. Este método combina técnicas de procesamiento de lenguaje natural y web scraping para recopilar y pre-procesar datos. Los resultados muestran sesgos importantes en los y las periodistas y el género de la fuente. De una muestra de 158 periodistas, 99 eran hombres (63%) y 59 mujeres (37%). De 12.113 noticias, 7.565 (62%) fueron escritas por hombres y 4.548 (38%) por mujeres periodistas. De las 12.334 fuentes mencionadas en la noticias, 9.771 eran hombres (79%) y 2.563 eran mujeres (21%). Un hallazgo significativo es que la igualdad en la sala de redacción está relacionada con la forma en que los y las periodistas eligen sus fuentes. Es decir, parece ser que cuando un medio tiene una redacción con igualdad de género, las fuentes de los y las periodistas también son más equitativas. Estos resultados son importantes para ser discutidos dentro de las escuelas de periodismo, para concientizar a los y las estudiantes sobre el sesgo de género en la profesión. Si aumentara la presencia de mujeres – como periodistas y como fuentes de información- en los medios, les permitiría aumentar su poder y estatus mediático.

The purpose of this study was to analyse online newspaper articles on students with intellectual disabilities (IDs) in order to identify related social phenomena to derive implications for inclusive education. Such study has traditionally practised through content analysis and/or discourse analysis manually, which is prone to subjective interpretation. Thus, this study implemented automated analysis to objectively select and interpret a big data. A total of 8,890 online newspaper articles that were published from 1990 to April 2019 were collected through automated parsing. The entire period and decade-phase based keyword and keyword network analysis were practised in order to determine how the social perceptions and related issues had changed over time. The results indicated that there was a rapid growth in scope of articles on students with IDs over the past 30 years. The attention of media gradually expanded from special education to improving quality of lives of students with IDs and their families. Moreover, online newspaper articles seemed to focus on social controversies and incidents such as sexual assaults that are related to students with IDs. Based on the results, ways to support inclusive education as well as social inclusion of students with IDs were discussed.

The purpose of this paper is to interpret the diffusion of innovation (transfer of opinions to the adoption category) from the simulation of opinion dynamics with five adapter categories set as agents, and to provide a computational social science method useful for marketing and mass media research. In the simulation, we observed the impact on the spread of innovation by manipulating variables such as the Initial Distribution of Opinions, the Confidence Coefficient between agents, the Mass Media Effects, and the Network Connection Probabilities of the random network. Simulation results show that when the media has a uniform impact on the market, the distribution of people's opinions is distorted in the direction that the media takes the lead. We also observed that by manipulating the initial values of the opinions of the initial adopters, the reliability coefficient, and the connection probability between the nodes of the random network, the market is affected, and the spread of innovation is affected.

Increasingly urbanized populations and climate change have shifted the focus of decision makers from economic growth to the sustainability and resilience of urban infrastructure and communities, especially when communities face multiple hazards and need to recover from recurring disasters. Understanding human behavior and its interactions with built environments in disasters requires disciplinary crossover to explain its complexity, therefore we apply the lens of complex adaptive systems (CAS) to review disaster studies across disciplines. Disasters can be understood to consist of three interacting systems: (1) the physical system, consisting of geological, ecological, and human-built systems; (2) the social system, consisting of informal and formal human collective behavior; and (3) the individual actor system. Exploration of human behavior in these systems shows that CAS properties of heterogeneity, interacting subsystems, emergence, adaptation, and learning are integral, not just to cities, but to disaster studies and connecting them in the CAS framework provides us with a new lens to study disasters across disciplines. This paper explores the theories and models used in disaster studies, provides a framework to study and explain disasters, and discusses how complex adaptive systems can support theory building in disaster science for promoting more sustainable and resilient cities.

With the increasing popularity of computational approaches to conduct social science research, building a scalable and efficient computing platform has become a topic of interest for academia to empower research labs and institutes to analyze large-scale data. While social science researchers have been very excited about the advancement of emerging technologies in big data, deep learning, computer vision, network analysis, etc., they are also constrained by the available computing resources to analyze data. This paper describes a scalable solution to deploy JupyterHub for computational social science research on the cloud. We use a reference architecture on AWS to walk through the design principles and details. Our architecture has helped facilitate several collaborations between Facebook and academia. The case study (Facebook Open Research and Transparency platform) shows that our architecture, using technologies like containerization and serverless computing, can support thousands of users to analyze web-scale datasets.

Computational methods have increased the objectivity of measures of human behavior and positioned personality science to benefit from the ongoing digital revolution. In this review, we define and discuss computational personality assessment (CPA), a measurement process that uses computational technologies to obtain estimates of personality. We briefly review some of the most promising sources of data currently used for CPA: mobile sensing, digital footprints from social media, images, language, and experience sampling. We present a concise overview of key findings, discuss the promise and opportunities of CPA (e.g., moving towards objective measures of personality, obtaining new insights from big data), and highlight important limitations and challenges in the development and application of CPA (e.g., establishing reliability and validity, selecting appropriate ground truth criterion, assessing affect and cognition, implications for ethics and privacy). We conclude with our perspective on how CPA could change our understanding of individual differences.

Addressing the problem of sudden food scarcity in US cities, and the up-and-coming field of computational social science. Hear the biggest stories from the world of science | 07 July 2021

Collective behavior provides a framework for understanding how the actions and properties of groups emerge from the way individuals generate and share information. In humans, information flows were initially shaped by natural selection yet are increasingly structured by emerging communication technologies. Our larger, more complex social networks now transfer high-fidelity information over vast distances at low cost. The digital age and the rise of social media have accelerated changes to our social systems, with poorly understood functional consequences. This gap in our knowledge represents a principal challenge to scientific progress, democracy, and actions to address global crises. We argue that the study of collective behavior must rise to a “crisis discipline” just as medicine, conservation, and climate science have, with a focus on providing actionable insight to policymakers and regulators for the stewardship of social systems.

With more and more voices and opinions entering the public domain, a key challenge facing journalists and editors is maximizing the context of the information that is presented on news websites. In this paper, we argue that systems for exposing readers to the many aspects of societal debates should be grounded in methods and tools that can provide a fine-grained understanding of these debates. The present article thereby explores the conceptual transition from opinion observation to opinion facilitation by introducing and discussing the Penelope opinion facilitator: a proof-of-concept reading instrument for online news media that operationalizes emerging methods for the computational analysis of cultural conflict developed in the context of the H2020 ODYCCEUS project. It will be demonstrated how these methods can be combined into an instrument that complements the reading experience of the news website The Guardian by automatically interlinking news articles on the level of semantic frames. In linguistic theory, semantic frames are defined as coherent structures of related concepts. We thereby zoom in on instances of the “causation” frame, such as “climate change causes global warming,” and illustrate how a reading instrument that links articles based on such frames might reconfigure our readings of climate news coverage, with specific attention to the case of global warming controversies. Finally, we relate our findings to the context of the development of computational social science, and discuss pathways for the evaluation of the instrument, as well as for the future upscaling of qualitative analyses and close readings. ispartof: Frontiers in Big Data vol:4 pages:1-10 ispartof: location:Switzerland status: published

In this review we discuss advances in the agent-based modeling of economic and social systems. We show the state of the art of the heuristic design of agents and how behavioral economics and laboratory experiments have improved the modeling of agent behavior. We further discuss how economic networks and social systems can be modeled and we discuss novel methodology and data sources. Lastly, we present an overview of estimation techniques to calibrate and validate agent-based models and show avenues for future research.

Data sharing, research ethics, and incentives must improve

Computational Social Science (CSS) is the use of Web Intelligence and the tools and technology capable of monitoring, analyzing, diagnosing, and resolving day-to-day problems of society. CSS is the development of intelligent systems and solutions to address the critical problems of the society such as poverty and hunger, slavery and torture, disease and suffering, and create tools that enable an illiterate person to be as productive as a PhD. The understanding was proposed and advocated by Dabbala Rajagopal (“Raj”) Reddy, who received the ACM Turing Award in 1994 for “pioneering the design and construction of large scaled artificial intelligence systems, demonstrating the practical importance and potential commercial impact of artificial intelligence technology” [1]. In a keynote speech delivered at the IEEE/WIC/ACM International Conference on Web Intelligence in Leipzig, Germany on August 24, 2017, Reddy pointed out that “Computer Science and Artificial Intelligence must embrace CSS as the next frontier in Web intelligence.” His vision on Artificial Intelligence and Web Intelligence for creating a truly humane society is both thought-provoking and instrumental to bringing about new revolutions in the two related fields.

This study explains how the economy affects the foreign policy rhetoric used by American presidents. When economic conditions deteriorate, presidents criticize foreign nations to boost thei...

In the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary ‘agents of change’ in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and learning plays an equally important role for the uptake of green technologies as economic considerations, and therefore in addition to carbon-price policies (top-down approach), implementing policies on education, social and cultural practices can significantly reduce residential carbon emissions.

Computational sociology leverages new tools and data sources to expand the scope and scale of sociological inquiry. It’s opening up an exciting frontier for sociologists of every stripe—from theorists and ethnographers to experimentalists and survey researchers. It expands the sociological imagination.

The possible existence of a research-practice gap is the topic of a long-standing debate in the field of public administration. In this Viewpoint essay, the authors examine the agendas of scholars and practitioners using the topic modeling technique of computational social science. Topic modeling a content analysis of 35 topics identified in Public Administration Review and PA Times (3,796 articles) showed that just over 50 percent of topics were common to both groups, indicating shared interests. There were, however, topics that the two groups distinctly focused on. Moreover, scholars and practitioners attached significant differences to the weights allocated to the prominent topics in their writing. Taken together, these findings indicate that topic modeling can shed new light on the research-practice gap in public administration.

This paper describes an approach integrating cognitive engineering with computational social science in the context of open source software (OSS) development. Through an analysis of large-scale collaborations in a complex operational setting, we study how expertise and task complexity predict changes in productivity when knowledge loss occurs. Using team data from thousands of software files, we model the effects of expertise, complexity, and knowledge loss on productivity. On its own, knowledge loss had a negative effect on productivity, but this effect was reversed when knowledge loss was combined with high complexity and high numbers of newcomers, suggesting that experts are better able to utilize crowdsourced work. We identify opportunities for research to inform prediction of outcomes in OSS projects based on team and task characteristics and demonstrate the value of integrating cognitive engineering with computational social science to study collaborative work in sociotechnical systems.

Successful instances of interdisciplinary collaboration can eventually enter a process of disciplinarisation. This article analyses one of those instances: agent-based computational social science, an emerging disciplinary field articulated around the use of computational models to study social phenomena. The discussion centres on how, in knowledge transfer dynamics from traditional disciplinary areas, practitioners parsed several epistemic resources to produce new foundational disciplinary shared commitments, and how disagreements operated as a mechanism of differentiation in their production. Two parsing processes are examined to illustrate this claim. The first one is the parsing of the qualitative–quantitative dualism, arguably the most important methodological disagreement in social science. The second one is the parsing of prediction, a key value in contemporary science. The analysis evidences that disagreements have fostered both external and internal dynamics of differentiation in agent-based computational social science. The former have permitted a more efficient use of epistemic resources, whereas the latter have forced practitioners to modify the foundational narrative and the agenda of the field. © 2019, Springer Nature B.V.

Meaning may arise from an element's role or interactions within a larger system. For example, hitting nails is more central to people's concept of a hammer than its particular material composition or other intrinsic features. Likewise, the importance of a web page may result from its links with other pages rather than solely from its content. One example of meaning arising from extrinsic relationships are approaches that extract the meaning of word concepts from co-occurrence patterns in large, text corpora. The success of these methods suggest that human activity patterns may reveal conceptual organization. However, texts do not directly reflect human activity, but instead serve a communicative function and are usually highly curated or edited to suit an audience. Here, we apply methods devised for text to a data source that directly reflects thousands of individuals' activity patterns, namely supermarket purchases. Using product co-occurrence data from nearly 1.3m shopping baskets, we trained a topic model to learn 25 high-level concepts (or "topics"). These topics were found to be comprehensible and coherent by both retail experts and consumers. Topics ranged from specific (e.g., ingredients for a stir-fry) to general (e.g., cooking from scratch). Topics tended to be goal-directed and situational, consistent with the notion that human conceptual knowledge is tailored to support action. Individual differences in the topics sampled predicted basic demographic characteristics. These results suggest that human activity patterns reveal conceptual organization and may give rise to it., Comment: 7 pages, 4 figures

Computational Communication Research (CCR) is a new open access journal dedicated to publishing high quality computational research in communication science. This editorial introduction describes the role that we envision for the journal. First, we explain what computational communication science is and why a new journal is needed for this subfield. Then, we elaborate on the type of research this journal seeks to publish, and stress the need for transparent and reproducible science. The relation between theoretical development and computational analysis is discussed, and we argue for the value of null-findings and risky research in additive science. Subsequently, the (experimental) two-phase review process is described. In this process, after the first double-blind review phase, an editor can signal that they intend to publish the article conditional on satisfactory revisions. This starts the second review phase, in which authors and reviewers are no longer required to be anonymous and the authors are encouraged to publish a preprint to their article which will be linked as working paper from the journal. Finally, we introduce the four articles that, together with this Introduction, form the inaugural issue.

We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of social class as an empirical case. Word embeddings represent semantic relations between words as relationships between vectors in a high-dimensional space, specifying a relational model of meaning consistent with contemporary theories of culture. Dimensions induced by word differences ( rich – poor) in these spaces correspond to dimensions of cultural meaning, and the projection of words onto these dimensions reflects widely shared associations, which we validate with surveys. Analyzing text from millions of books published over 100 years, we show that the markers of class continuously shifted amidst the economic transformations of the twentieth century, yet the basic cultural dimensions of class remained remarkably stable. The notable exception is education, which became tightly linked to affluence independent of its association with cultivated taste.

Contagion processes have been widely studied in epidemiology and life science in general, but their implications are largely tangible in other research areas, such as in network science and computational social science. Contagion models, in particular, have proven helpful in the study of information diffusion, a very topical issue thanks to its applications to social media/network analysis, viral marketing campaigns, influence maximization and prediction. In bibliographic networks, for instance, an information diffusion process takes place when some authors, that publish papers in a given topic, influence some of their neighbors (coauthors, citing authors, collaborators) to publish papers in the same topic, and the latter influence their neighbors in their turn. This well-accepted definition, however, does not consider that influence in bibliographic networks is a complex phenomenon involving several scientific and cultural aspects. In fact, in scientific citation networks, influential topics are usually considered those ones that spread most rapidly in the network. Although this is generally a fact, this semantics does not consider that topics in bibliographic networks evolve continuously. In fact, knowledge, information and ideas are dynamic entities that acquire different meanings when passing from one person to another. Thus, in this paper, we propose a new definition of influence that captures the diffusion of inspiration within the network. We call it inspiration score, and show its effectiveness in detecting the most inspiring topics, authors, papers and venues in a citation network built upon two large bibliographic datasets. We show that the inspiration score can be used as an alternative or complementary bibliographic index in academic ranking applications.

Many hoped that social networking sites would allow for the open exchange of information and a revival of the public sphere. Unfortunately, conversations on social media are often toxic and not conducive to healthy political discussions. Twitter, the most widely used social network for political discussions, doubled the limit of characters in a tweet in November 2017, which provided an opportunity to study the effect of technological affordances on political discussions using a discontinuous time series design. Using supervised and unsupervised natural language processing methods, we analyzed 358,242 tweet replies to U.S. politicians from January 2017 to March 2018. We show that doubling the permissible length of a tweet led to less uncivil, more polite, and more constructive discussions online. However, the declining trend in the empathy and respectfulness of these tweets raises concerns about the implications of the changing norms for the quality of political deliberation.

As network research becomes more sophisticated, it is more common than ever for researchers to find themselves not studying a single network but needing to analyze sets of networks. An important task when working with sets of networks is network comparison, developing a similarity or distance measure between networks so that meaningful comparisons can be drawn. The best means to accomplish this task remains an open area of research. Here we introduce a new measure to compare networks, the Network Portrait Divergence, that is mathematically principled, incorporates the topological characteristics of networks at all structural scales, and is general-purpose and applicable to all types of networks. An important feature of our measure that enables many of its useful properties is that it is based on a graph invariant, the network portrait. We test our measure on both synthetic graphs and real world networks taken from protein interaction data, neuroscience, and computational social science applications. The Network Portrait Divergence reveals important characteristics of multilayer and temporal networks extracted from data., 22 pages (double-spaced), 7 figures

Social networks are present in our everyday lives, not just in face to face communication, but also when we communicate through the Internet. The latter leaves massive traces of data and thereby opens opportunities to acquire a better understanding of social communities in general. In this work, we are particularly interested in the patterns of volunteer efforts within the communities. To that end, we examine the community structure of several large online question–answer sites and how they evolve over time. To describe the user interaction patterns concisely, we employ the hyperbolic community model. This statistical model allows for a summary of each community in each time step by means of intuitive parameters that reflect the connectivity pattern within the network. Our study of the temporal evolution of these parameters reveals an important characteristic: In contrast to what has been observed earlier in the analyses of growth behaviour of online communities, we observe that the user activity within a community is constant with respect to its size throughout its lifetime. Furthermore, the structural organisation of different communities across different question–answer sites seems to follow a common scheme: There is a small group of users who is responsible for the majority of the social interactions.

This position paper is based on a keynote presentation at the COLING 2016 Workshop on Language Technology for Digital Humanities in Osaka, Japan. It departs from observations about working practices in Humanities disciplines following a hermeneutic tradition of text interpretation versus the method-oriented research strategies in Computational Linguistics (CL). The respective praxeological traditions are quite different. Yet more and more researchers are willing to open up towards truly transdisciplinary collaborations, trying to exploit advanced methods from CL within research that ultimately addresses questions from the traditional Humanities disciplines and the Social Sciences. The article identifies two central workflow-related issues for this type of collaborative project in the Digital Humanities (DH) and Computational Social Science: (1) a scheduling dilemma, which affects the point in the course of the project when specifications of the core analysis task are fixed (as early as possible from the computational perspective, but as late as possible from the Humanities perspective) and (2) the subjectivity problem, which concerns the degree of intersubjective stability of the target categories of analysis. CL methodology demands high inter-annotator agreement and theory-independent categories, while the categories in hermeneutic reasoning are often tied to a particular interpretive approach (viz. a theory of literary interpretation) and may bear a non-trivial relation to a reader’s pre-understanding. Building a comprehensive methodological framework that helps overcome these issues requires considerable time and patience. The established computational methodology has to be gradually opened up to more hermeneutically oriented research questions; resources and tools for the relevant categories of analysis have to be constructed. This article does not call into question that well-targeted efforts along this path are worthwhile. Yet, it makes the following additional programmatic point regarding directions for future research: It might be fruitful to explore—in parallel—the potential lying in DH-specific variants of the concept of rapid prototyping from Software Engineering. To get an idea of how computational analysis of some aspect of text might contribute to a hermeneutic research question, a prototypical analysis model is constructed, e.g., from related data collections and analysis categories, using transfer techniques. While the initial quality of analysis may be limited, the idea of rapid probing allows scholars to explore how the analysis fits in an actual workflow on the target text data and it can thus provide early feedback for the process of refining the modeling. If the rapid probing method can indeed be incorporated in a hermeneutic framework to the satisfaction of well-disposed Humanities scholars, a swifter exploration of alternative paths of analysis would become possible. This may generate considerable additional momentum for transdisciplinary integration. It is as yet too early to point to truly Humanities-oriented examples of the proposed rapid probing technique. To nevertheless make the programmatic idea more concrete, the article uses two experimental scenarios to argue how rapid probing might help addressing the scheduling dilemma and the subjectivity problem respectively. The first scenario illustrates the transfer of complex analysis pipelines across corpora; the second one addresses rapid annotation experiments targeting character mentions in literary text.

Comparing and contrasting qualitative and quantitative methods for social media data exploration, this article describes and demonstrates the topic modeling approach for the descriptive analysis of...

When sharing personal details, versus talking about others, consumers tend to accentuate the positive experiences they have with brands in order to self-enhance amongst friends. Consumers also take...

Computational social science has caused a shift of research paradigm in social science in general and communication in particular. The special issue brings together a community of active researcher...

Is it possible to judge someone accurately from his or her online activity? The Internet provides vast opportunities for individuals to present themselves in different ways, from simple self-enhancement to malicious identity fraud. We often rely on our Internet-based judgments of others to make decisions, such as whom to socialize with, date, or employ. Recently, personality-perception researchers have turned to studying social media and digital devices in order to ask whether a person’s digital traces can reveal aspects of his or her identity. Simultaneously, advances in “big data” analytics have demonstrated that computer algorithms can predict individuals’ traits from their digital traces. In this article, we address three questions: What do we currently know about human- and computer-based personality assessments? How accurate are these assessments? Where are these fields heading? We discuss trends in the current findings, provide an overview of methodological approaches, and recommend directions for future research.

Erick Elejalde obtained his Ph.D. in Computer Science from the University of Concepcion, Chile in 2018. His thesis focuses on analyzing the behavior of the mass media on-line and testing socioeconomic theories using computational methods. His research interests include computational social science, online media-behavior modeling, and social networks. He works now as a research assistant at the Data Science Institute, Universidad del Desarrollo, Santiago, Chile.

Computational social science is the study that focuses on developing and analyzing computation models for social issues. Elections are major social events that impact society. Behavioral analysis i...