Computational Social Science Approach Research Articles

The Reddit cannabis subjective highness rating scale: Applying computational social science to explore psychological and environmental correlates of naturalistic cannabis use.

Social media data provide unprecedented access to discussions of active, naturalistic, and often real-time cannabis use in an era of cannabis policy liberalization. The aim of this study was to explore psychological and environmental correlates of cannabis effects by applying computational social science approaches to a large dataset of unprompted reports of naturalistic cannabis use with corresponding self-reported numerical ratings of subjective highness. Post title text was extracted via the Pushshift dataset from N = 328,865 posts to the r/trees Reddit community, where posters self-assess and disclose how high they feel on a scale from 1 to 10 (M = 6.9, SD = 1.8). Structural topic modelling and Linguistic Inquiry and Word Count (LIWC) dictionary-based approaches were applied to identify (1) frequently discussed topics and (2) text indicative of 5 psychological processes (affective, social, cognitive, perceptual, biological), respectively, as well as to examine relationships between subjective highness and (1) topic prevalence and (2) psychological process word counts. A 40-topic model was selected for interpretation based on semantic coherence and exclusivity. The most discussed topics in a 40-topic model were characterized by references to smoking places, social contexts, positive affect, cognitive states, as well as food and media consumed. In LIWC dictionary analyses, words mentioning affective, social, and cognitive processes were referenced more often than perceptual or body processes. Posters reported greater subjective highness when using language that referred to in-person social environments and lower subjective highness when using language that referred to online social environments and positive affect psychological states. This examination of unprompted online reports of naturalistic cannabis use identified textual content referring to affect and to other people as being associated with perceived effects of cannabis. These affective and social aspects of the cannabis use experience were salient to active posters in this online community and should be integrated into experience sampling methods and behavioral pharmacology research, as well as public health messaging.

How to measure political polarization in text-as-data? A scoping review of computational social science approaches

ABSTRACT The rise of political polarization within western societies has been portrayed by events such as the United States Capitol riot or the United Kingdom’s exit from the European Union. In this context, we argue that computational social science (CSS) methods offer a scalable and language-independent fashion to measure political polarization, enabling the processing of big data. In this vein, this article offers the first scoping review of the application of CSS methods to analyzing political polarization through text as data. We propose a categorization framework and reflect on the advantages and disadvantages of the different models used in the literature. Additionally, we underline the importance of filling research gaps, such as considering the temporal characteristic of political polarization, using a mathematical approach to analyze the use cases, and avoiding location and platform bias. We also provide recommendations for future research.

A computational social science approach to understanding predictors of Chafee service receipt

The John H. Chafee Foster Care Program for Successful Transition to Adulthood (CFCIP) allocates funding to provide services to youth who are likely to age out of foster care. These services, covering everything from mentoring to financial aid, are expected to be distributed in ways that prepare youth for life after care. One natural question to ask is, which youth receive Chafee services? The present work makes use of the National Youth in Transition Database (NYTD), a large-scale administrative dataset that tracks services allocated to youth that use CFCIP funds to answer this question. Specifically, we conduct a forensic social science analysis of the NYTD data. To do so, we first use computational methods to help us uncover the factors that best predict which youth will receive services associated with service receipt. We find that the majority of variables in the Adoption and Foster Care Analysis and Reporting System (AFCARS) and NYTD have limited or no utility in predicting Chafee service receipt, and that a subset of three variables—youth age, youth time in care, and the state in which a youth is in care—explain almost all variability in service receipt. We conclude with a discussion of the implications of these and other findings on future research on Chafee service allocation, and the utility of predictive modeling in child welfare, with a particular focus on the utility of the NYTD in this context.

The Governance Cycle in Parliamentary Democracies: A Computational Social Science Approach by Scott de Marchi and Michael Laver

The Governance Cycle in Parliamentary Democracies: A Computational Social Science Approach <i>by Scott de Marchi and Michael Laver</i>

Understanding and Improving Social Factors in Education: A Computational Social Science Approach

Understanding and Improving Social Factors in Education: A Computational Social Science Approach

Gender (in)equality in Chilean press: journalists and sources

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.

Phase Transition in the Social Impact Model of Opinion Formation in Log-Normal Networks

People may change their opinions as a consequence of interacting with others. In the literature, this phenomenon is expressed as opinion formation and has a wide range of applications, including predicting social movements, predicting political voting results, and marketing. The interactions could be face-to-face or via online social networks. The social opinion phases are categorized into consensus, majority, and non-majority. In this research, we study phase transitions due to interactions between connected people with various noise levels using agent-based modeling and a computational social science approach. Two essential factors affect opinion formations: the opinion formation model and the network topology. We assumed the social impact model of opinion formation, a discrete binary opinion model, appropriate for both face-to-face and online interactions for opinion formation. For the network topology, scale-free networks have been widely used in many studies to model real social networks, while recent studies have revealed that most social networks fit log-normal distributions, which we considered in this study. Therefore, the main contribution of this study is to consider the log-normal distribution network topology in phase transitions in the social impact model of opinion formation. The results reveal that two parameters affect the phase transition: noise level and segregation. A non-majority phase happens in equilibrium in high enough noise level, regardless of the network topology, and a majority phase happens in equilibrium in lower noise levels. However, the segregation, which depends on the network topology, affects opinion groups’ population. A comparison with the scale-free network topology shows that in the scale-free network, which have a more segregated topology, resistance of segregated opinion groups against opinion change causes a slightly different phase transition at low noise levels. EI (External-Internal) index has been used to measure segregations, which is based on the difference between between-group (External) links and within-group (Internal) links.

Methods, Developments, and Technological Innovations for Population Surveys

This article reviews contemporary issues in survey research, connecting established methods to innovative tools and technologies like real-time sensors and computer vision. This link takes into account the idea about the “organical” nature of Big Data, which represents a challenge toward a modernization of population studies in the light of technological innovations. The adopted dominant paradigm of data gathering is web survey (computer-assisted web interviewing), which is explored through the formalization of chain-referral methods as respondent-driven sampling. The general orientation is toward a computational social science approach. Weaknesses of such methodology is studied and solutions are provided with insights from empirical research on panel management. Contribution from gamification techniques is critically discussed.

In search of bluer skies: Would people move to places of better air qualities?

This study examines the statistical relationship between people's movements and difference in air quality between the origin and the destination, using computational social science approach. We put forward an important environmentally driven human movement question: Would people move to places of better air qualities? Utilizing Twitter Streaming API, 39,928,132 randomized geo-coded tweets posted by 8,551,557 individual users during the period of 01/01/2016 – 31/12/2017 were extracted to examine the movements of Twitter users. Coupling Twitter users’ international movements with air quality data, our results have shown that Twitter users tend to move to places of better air qualities in general. We find that in most countries, the net move-out rate tends to be positively correlated with the monthly PM2.5 concentration in the origin country, and the number of international movements to the destination country is negatively correlated with the decrease in PM2.5 concentration in the origin country. For domestic movements, our results have shown that Twitter users in Indian cities, including Mumbai, one of the largest and highly polluted cities, tend to move to other Indian cities of better air qualities. Our results confirm that people across different parts of the world tend to move to places of better air qualities.

Information diffusion between Dutch cities: Revisiting Zipf and Pred using a computational social science approach

News travels fast and far, and the general idea is that the spatial extent of news coverage has increased over time. Information flows are always involved in systems of interdependent cities. This is the reason why George Zipf and Allan Pred, both pioneers of the urban systems literature, were eager to obtain data on these relations to understand urban system dynamics. However, because of limited resources in data acquisition, they restricted their studies to small samples of cities or short periods of time. By using novel computational social science techniques on a digital archive of historical newspapers, we could map and explore changes in the spatial extent of news coverage in the Netherlands at an unprecedented detailed scale for a period of 62 years. In this paper, we analyse 24 million news items mentioning 312 different cities and towns in a sample of 31 local newspapers. Thanks to this data, we were able to reconstruct the information field of urban readerships from different cities and how it changed over time. By analysing their evolution, we find evidence of space-time contraction with an increasing coverage of faraway places in the period ranging from 1869 to 1930. However, this coverage is not evenly distributed but is characterized by a hierarchical selection process. Coverage of the largest cities in the Randstad increased at the expense of information flows from intermediate provincial cities. More generally, this paper shows how computational social science approaches may offer new ways of looking at urban dynamics with large text corpora such as digital archives of historical newspapers.

Do messages spread widely also diffuse fast? Examining the effects of message characteristics on information diffusion

Research examining the information diffusion on social media focused on the size of diffusion but has largely missed the speed by which information spreads over time. To fill this void, this study explores what intrinsic message characteristics drive information not only to reach many people but also to spread fast across social networks. Using a computational social science approach, this study collected all the original tweets from the official Twitter account of the Centers for Disease Control and Prevention (CDC) for 12 months (N = 1934). The diffusion of each tweet message was tracked for six days following its publication. The results showed that tweets with severity, efficacy, and call-for-action information achieved greater diffusion sizes and faster speed. Negative emotional words drove the information to spread widely and quickly, while positive emotional words suppressed the diffusion. Tweet messages with less affiliative words were linked to a larger size and faster speed. The study adds new insights into the dynamic processes of information diffusion and provides practical guidance to promote widespread and fast diffusion on social media.

Complex systems analysis of hybrid warfare

Being empowered with the appropriate toolset will enable decision-makers to analyze how best to intervene in ever-changing complex systems. This research project explored deconstructing qualitative methods to identify and document requirements to connect the models to computational social science approaches. Previous efforts from our research provided a customizable toolset that assesses the current and future impact that decisions, policies, or strategies can deliver in a system to tackle particularly complex problems. This paper presents a portion of the research effort that developed a threat analysis framework by establishing formally documented research methods to effectively combine conceptual and computational tools. This enables more accurate, efficient, and foresightful knowledge capture and depictions of a particular problem space. The case that the tools and approach are tested against is Russia’s application of grey zone warfare tools in Moldova.

Characterizing negative sentiments in at-risk populations via crowd computing: a computational social science approach

Drawing on psychological theory, we created a new approach to classify negative sentiment tweets and presented a subset of unclassified tweets to humans for categorization. With these results, a tweet classification distribution was built to visualize how the tweets can fit in different categories. The approach developed through visualization and classification of data could be an important base to measure the efficiency of a machine classifier with psychological diagnostic criteria as the base (Thelwall et al. in J Assoc Inf Sci Technol 62(4):406–418, 2011). Nonetheless, this proposed system is used to identify red flags in at-risk population for further intervention, due to the need to be validated through therapy with an expert.

Dynamic Spirals Put to Test: An Agent-Based Model of Reinforcing Spirals Between Selective Exposure, Interpersonal Networks, and Attitude Polarization

Within the context of partisan selective exposure and attitude polarization, this study investigates a mutually reinforcing spiral model, aiming to clarify mechanisms and boundary conditions that affect spiral processes—interpersonal agreement and disagreement, and the ebb and flow of message receptions. Utilizing agent-based modeling (ABM) simulations, the study formally models endogenous dynamics of cumulative processes and its reciprocal effect of media choice behavior over extended periods of time. Our results suggest that interpersonal discussion networks, in conjunction with election contexts, condition the reciprocal effect of selective media exposure and its attitudinal consequences. Methodologically, results also highlight the analytical utility of computational social science approaches in overcoming the limitations of typical experimental and observations studies.

The impact of individual versus group rewards on work group performance and cooperation: A computational social science approach

PurposeTo examine the effect of individual versus group evaluation and reward systems on work group behavior and performance under different task conditions. MethodologyUses computational social methods using Agent Based Models to simulate work group interactions as different forms of iterated games. FindingsGroup based systems outperform individual based and mixed systems, producing more cooperative behavior, the best performing groups and individuals in most types of interaction games. A new role emerges, the self-sacrificer, who plays a critical role in enabling other group members and the group, to perform better at their own expense. Research ImplicationsSuggest opportunities for model development and guidelines for designing real world experiments. Practical ImplicationsHelps firms engineer better performing work groups as well as the design of other business systems. Social ImplicationsIdentifies mechanisms by which cooperation can be developed in social systems. Originality/ValueDemonstrates the role and value of computational social science methods and agent based models to business research.

A (computational) social science perspective on societal transitions

This paper aims to illustrate how social sciences, sociology in particular, have theorized on societal transitions. The first section introduces some preliminary definitions. The assumption is that a societal transition is more than a social, economic or technological change. It is a large-scale and long-term macro process through which a given social system radically changes its structural basis, in terms of new socio-technical practices, governance rules, social and economic institutions, cultural frames, and patterns of social life. The second section provides an excursus on social science accounts on transitions. In particular, the attention has been given to Norbert Elias’ famous study on the rise and fall of the court society in France, between 17th and 18th century and to Manuel Castells’ recent analysis of the emergence of the network society. The third section discusses problems and challenges of standard approaches and suggests some building blocks of societal transition models, taken from complexity and social simulation literature. The concluding section suggests some way-forward measures towards a computational social science approach to societal transitions.