Large-scale Social Systems Research Articles

Quasi-critical dynamics in large-scale social systems regulated by sudden events.

How do heterogeneous individual behaviors arise in response to sudden events and how do they shape large-scale social dynamics? Based on a five-year naturalistic observation of individual purchasing behaviors, we extract the long-term consumption dynamics of diverse commodities from approximately 2.2 million purchase orders. We subdivide the consumption dynamics into trend, seasonal, and random components and analyze them using a renormalization group. We discover that the coronavirus pandemic, a sudden event acting on the social system, regulates the scaling and criticality of consumption dynamics. On a large time scale, the long-term dynamics of the system, regardless of arising from trend, seasonal, or random individual behaviors, is pushed toward a quasi-critical region between independent (i.e., the consumption behaviors of different commodities are irrelevant) and correlated (i.e., the consumption behaviors of different commodities are interrelated) phases as the pandemic erupts. On a small time scale, short-term consumption dynamics exhibits more diverse responses to the pandemic. While the trend and random behaviors of individuals are driven to quasi-criticality and exhibit scale-invariance as the pandemic breaks out, seasonal behaviors are more robust against regulations. Overall, these discoveries provide insights into how quasi-critical macroscopic dynamics emerges in heterogeneous social systems to enhance system reactivity to sudden events while there may exist specific system components maintaining robustness as a reflection of system stability.

Epistemic outsiders: Unpacking and utilising the epistemic dimension of disruptive agency in sustainability transformations

Disruptions (systemic disturbances) are crucial to initiate and accelerate sustainability transformations of large-scale social systems (be they socio-ecological, socio-technical, or socio-institutional). Their emergence, characteristics and effects strongly relate to the role of agents who aim to disrupt and transform the status quo, and which thus possess what we call disruptive agency. In this paper, we highlight the epistemic dimension of disruptive agency in social transformations, first by conceptualizing disruptive agents as epistemic outsiders with respect to the social system that they intend to disrupt and transform, and second by connecting this conceptualization to notions of belief, social practices, social networks, discourses, or institutions. We identify five advantages of this approach. Firstly, it informs and conceptually enables various promising interdisciplinary avenues to explore and potentially influence transformative change towards sustainability. Secondly, an epistemic conception of disruptive agency offers a key for an integrated analysis of the individual and collective levels of agency involved in sustainability transformations. Thirdly, the notion of epistemic outsiders conceptually connects agent positions across system boundaries that are understood to be of crucial importance for sustainability transformations respectively (e.g., “niche innovators” or “regime intermediaries”) but which lack an integrated understanding. Fourthly, an epistemic perspective additionally highlights the changing requirements and challenges resulting in two principal stages of transformations unfolding over time, namely before/after a new epistemic layout is shared by a majority of agents. Finally, the above features allow to derive and conceive of new intervention formats and strategies.

Language Matters In Twitter: A Large Scale Study

Despite the widespread adoption of Twitter internationally, little research has investigated the differences among users of different languages. In prior research, the natural tendency has been to assume that the behaviors of English users generalize to other language users. We studied 62 million tweets collected over a four-week period and found that more than 100 languages were used. Only half of the tweets were in English (51%). Other popular languages including Japanese, Portuguese, Indonesian, and Spanish together accounted for 39% of the tweets. Examining users of the top 10 languages, we discovered cross-language differences in adoption of features such as URLs, hashtags, mentions, replies, and retweets. We discuss our work’s implications for research on large-scale social systems and design of cross-cultural communication tools.

Combating Threats to Collective Attention in Social Media: An Evaluation

Breaking news, viral videos, and popular memes are all examples of the collective attention of huge numbers of users focusing in large-scale social systems. But this self-organization, leading to user attention quickly coalescing and then collectively focusing around a phenomenon, opens these systems to new threats like collective attention spam. Compared to many traditional spam threats, collective attention spam relies on the insidious property that users themselves will intentionally seek out the content where the spam will be encountered, potentially magnifying its effectiveness. Our goal in this paper is to initiate a study of this phenomenon. How susceptible are social systems to collective attention threats? What strategies by malicious users are most effective? Can a system automatically inoculate itself from emerging threats? Towards beginning our study of these questions, we take a two fold approach. First, we develop data-driven models to simulate large-scale social systems based on parameters derived from a real system. In this way, we can vary parameters — like the fraction of malicious users in the system, their strategies, and the countermeasures available to system operators — to explore the resilience of these systems to threats to collective attention. Second, we pair the data-driven model with a comprehensive evaluation over a Twitter system trace, in which we evaluate the effectiveness of countermeasures deployed based on the first moments of a bursting phenomenon in a real system. Our experimental study shows the promise of these countermeasures to identifying threats to collective attention early in the lifecycle, providing a shield for unsuspecting social media users.

Mental Health and Wealth: Depression, Gender, Poverty, and Parenting.

Depression is a common and debilitating condition that adversely affects functioning and the capacity to work and establish economic stability. Women are disproportionately burdened by depression, and low-income pregnant and parenting women have particularly high rates of depression and often lack access to treatment. As depression can be treated, it is a modifiable risk factor for poor economic outcomes for women, and thus for children and families. Recent national and state health care policy changes offer the opportunity for community-based psychological and economic interventions that can reduce the number of pregnant and parenting women with clinically significant depressive symptoms. Moreover, there is strong evidence that in addition to benefiting women's well-being, such reforms bolster children's emotional and social development and learning and help families rise out of poverty. This review summarizes the mental health and economic literature regarding how maternal depression perpetuates intergenerational poverty and discusses recommendations regarding policies to treat maternal depression in large-scale social services systems.

A general framework of covid-19 model with control strategies: the application of large-scale social restrictions, self-quarantine, and immune system improvement in Surabaya, Indonesia

A covid-19 is a virus disease of worldwide importance which spread not only in a small country such as Mongolia but also in a large country like the United State. It is caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and well known as droplet-borne disease. The droplets containing coronavirus are spread through the air within a meter and deposited on the mucous membranes of suscpetible person. The aim of this study was to produce a general framework model of corona virus disease with the application of large-scale social restriction, self-quarantine and immune system improvement as control strategies to reduce the spread of the disease. This study was conducted in Surabaya, Indonesia with a total of 299 positive patients infected with covid-19 until the fourth week of April, 2020. The transmission routes of the disease had been constructed based on the real problem. Therefore, human population have been divided into some sub-population based on the characteritics of disease symptoms. The spread of covid-19 have been formulated mathematically by using compartment model. Stability analysis had been done for a free disease equilbrium and an endemic equilibrium. Moreover, numerical simulation were also provided to quantify the disease dynamics.

Tinjauan Hukum Islam Pada Edaran Pemerintah dan MUI Dalam Menyikapi Wabah Covid-19 Setelah Pemberlakuan New Normal

This study discusses the Covid-19 virus that is spreading in the world and its handling from the government and MUI after the implementation of new normal according to Islamic law. The government implements a large-scale social restrictions system (PSBB) or social distancing to break the chain of the spread of the covid-19 virus. The government has implemented new normal rules. MUI has issued a notice numbered Kep-1188 / DP-MUI / V / 2020 concerning new normalcy that will be applied by the government such as reopening places of worshipaccording to the health protocol. Nevertheless, this circular is a pros and cons for some people. The purpose of this study is to help the public understand whether the government and MUI circulars in tackling the plague after applying the new normal according to the Shari'a or actually contrary to Islamic Sharia. The research method is the study of literature literature with a normative approach and historical approach. The results of the study showed that the rules imposed by the government and MUI in dealing with the outbreak of Covid-19 pacsa new normal did not disregard Islamic law.

Development of Content Security Based on Artificial Intelligence

Content security refers to the protection of information content and that the information content meets the requirements at political, legal, and moral levels. The recent development of artificial intelligence (AI) has had a very important impact on content security. In this article, we summarize the research status and development trends of AI-based content security in China and abroad based on the major strategic demand therefor, and presents the key technical issues regarding AI-based content security. This study proposes to build the world’s leading AI-based content security system through a three-step strategy. Innovation and breakthroughs should be made in areas such as adversarial machine learning, explainable AI, hybrid enhanced intelligence, and knowledge-driven content security. Meanwhile, the construction of policies, regulations, and regulatory mechanisms should be emphasized. Furthermore, major content security infrastructure such as cyber ranges for content attack and defense and large-scale social system simulation devices for public opinion attack and defense should be established.

Social Institutions Transformation as a Factor of the Social and Economic Development of Modern Russia

The systemic determinacy of modern Russian society in time and space coordinates allows us to conclude that it is at the process stage of the life cycle of the large-scale social and economic systems functioning within the stage of the digital age that is characterized by the features of environmental systems. It means that the logic of the social and economic development of modern Russia is to ensure the harmonization of the performance and state of all economic systems through communication and coordination, as well as creating conditions for transactions. Following this logic, the article proves the possibility of considering it as a factor providing conditions for socio-economic development using the example of the transformation of the public administration institution. The article defines the subject-object and structural content of the public administration institution. By using the matrix approach, the authors characterize the evolutionary dynamics of the public administration institution. It makes it possible to draw a conclusion about the formation within the local civilizational matrix of modern Russia of a new type of the institutional core – the intellectual one, in which basic and compensatory institutions of the market and distribution types are integrated. The article presents a methodology that allows determining the degree of the formation of public administration, as well as assessing its impact on the social and economic development ofmodern Russia.

Analysis on the Information Management of Social Security

The social security undertaking is a relatively complicated and large-scale social system project. It requires multiple government departments to cooperate with each other and participate in management. Therefore, problems such as poor information communication, unclear division of duties, and improper data storage are inevitable. And the management agencies caused a lot of inconvenience. Many insured people could not receive insurance benefits on time due to various reasons, which caused inconvenience to the people’s life and increased social unrest factors.

Where the smart things are: social machines and the Internet of Things

The emergence of large-scale social media systems, such as Wikipedia, Facebook, and Twitter, has given rise to a new multi-disciplinary effort based around the concept of social machines. For the most part, this research effort has limited its attention to the study of Web-based systems. It has also, perhaps unsurprisingly, tended to highlight the social scientific relevance of such systems. The present paper seeks to expand the scope of the social machine research effort to encompass the Internet of Things. One advantage of this expansion is that it helps to reveal some of the links between the science of social machines and the sciences of the mind. A second advantage is that it furthers our conceptual understanding of social machines and supports the quest to derive a philosophically-robust definition of the term “social machine.” The results of the present analysis suggest that social machines are best conceived as systems in which a combination of social and technological elements play a role in the mechanistic realization of system-level phenomena. The analysis also highlights the relevance of cognitive science and the philosophy of mind to our general understanding of systems that transcend the cyber, physical, and social domains.

Security strategies of indigenous women in Nenets Autonomous Region, Russia

ABSTRACTThis paper discusses different strategies used by indigenous women seeking social security in Nenets Autonomous Region, Russia. Social security is understood as state provision as well as cultural institutions and efforts of individuals to overcome insecurity. One case describes the history of a woman who after the loss of her husband resorted to a traditional solution: she moved in to join the household of the deceased husband’s brothers in the tundra. In the second case, a woman leaves her baby in a orphanage for a few months. This latter strategy shows how women are able to preserve a high birth-rate while sustaining a tundra-based life. Even though these choices are seen within the dichotomy of tradition and modernity, which is central to existing ethnographies, this paper attempts to take the analysis further. I apply anthropological insights about care as a process that reveals social bonds, group belonging and identity in different settings, from the more intimate ties within kin groups to large-scale social systems such as state institutions. My goal is to contribute to the study of the interconnectedness among bonds, relations and affective landscapes on different levels – from mother–children bonds, to the nuclear family to community and state institutions.

Beautiful and Damned. Combined Effect of Content Quality and Social Ties on User Engagement

User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects are rarely explored jointly and at scale. By looking at how users generate and access pictures of varying beauty on Flickr, we investigate how the production of quality impacts the dynamics of online social systems. We develop a deep learning computer vision model to score images according to their aesthetic value and we validate its output through crowdsourcing. By applying it to over 15B Flickr photos, we study for the first time how image beauty is distributed over a large-scale social system. Beautiful images are evenly distributed in the network, although only a small core of people get social recognition for them. To study the impact of exposure to quality on user engagement, we set up matching experiments aimed at detecting causality from observational data. Exposure to beauty is double-edged: following people who produce high-quality content increases one's probability of uploading better photos; however, an excessive imbalance between the quality generated by a user and the user's neighbors leads to a decline in engagement. Our analysis has practical implications for improving link recommender systems.

Coarse cluster enhancing collaborative recommendation for social network systems

Traditional collaborative filtering based recommender systems for social network systems bring very high demands on time complexity due to computing similarities of all pairs of users via resource usages and annotation actions, which thus strongly suppresses recommending speed. In this paper, to overcome this drawback, we propose a novel approach, namely coarse cluster that partitions similar users and associated items at a high speed to enhance user-based collaborative filtering, and then develop a fast collaborative user model for the social tagging systems. The experimental results based on Delicious dataset show that the proposed model is able to dramatically reduce the processing time cost greater than 90% and relatively improve the accuracy in comparison with the ordinary user-based collaborative filtering, and is robust for the initial parameter. Most importantly, the proposed model can be conveniently extended by introducing more users’ information (e.g., profiles) and practically applied for the large-scale social network systems to enhance the recommending speed without accuracy loss.

Cooperation, conformity, and the coevolutionary problem of trait associations

In large scale social systems, coordinated or cooperative outcomes become difficult because encounters between kin or repeated encounters between friends are infrequent. Even punishment of noncooperators does not entirely alleviate the dilemma. One important mechanism for achieving cooperative outcomes in such social systems is conformist bias where individuals copy the behavior performed by the majority of their group mates. Conformist bias enhances group competition by both stabilizing behaviors within groups and increasing variance between groups. Due to this group competition effect, conformist bias is thought to have been an important driver of human social complexity and cultural diversity. However, conformist bias only evolves indirectly through associations with other traits, and I show that such associations are more difficult to obtain than previously expected. Specifically, I show that initial measures of population structure must be strong in order for a strong association between conformist bias and cooperative behaviors (cooperation and costly punishment) to evolve and for these traits to reach high frequencies. Additionally, the required initial level of association does not evolve de novo in simulations run over long timescales. This suggests that the coevolution of cooperative behaviors and conformist bias alone may not explain the high levels of cooperation within human groups, though conformist bias may still play an important role in combination with other social and demographic forces.

On the discovery of social roles in large scale social systems

The social role of a participant in a social system is a label conceptualizing the circumstances under which she interacts within it. They may be used as a theoretical tool that explains why and how users participate in an online social system. Social role analysis also serves practical purposes, such as reducing the structure of complex systems to rela- tionships among roles rather than alters, and enabling a comparison of social systems that emerge in similar contexts. This article presents a data-driven approach for the discovery of social roles in large scale social systems. Motivated by an analysis of the present art, the method discovers roles by the conditional triad censuses of user ego-networks, which is a promising tool because they capture the degree to which basic social forces push upon a user to interact with others. Clusters of censuses, inferred from samples of large scale network carefully chosen to preserve local structural prop- erties, define the social roles. The promise of the method is demonstrated by discussing and discovering the roles that emerge in both Facebook and Wikipedia. The article con- cludes with a discussion of the challenges and future opportunities in the discovery of social roles in large social systems.

Exploring the effect of power law social popularity on language evolution.

We evaluate the effect of a power-law-distributed social popularity on the origin and change of language, based on three artificial life models meticulously tracing the evolution of linguistic conventions including lexical items, categories, and simple syntax. A cross-model analysis reveals an optimal social popularity, in which the λ value of the power law distribution is around 1.0. Under this scaling, linguistic conventions can efficiently emerge and widely diffuse among individuals, thus maintaining a useful level of mutual understandability even in a big population. From an evolutionary perspective, we regard this social optimality as a tradeoff among social scaling, mutual understandability, and population growth. Empirical evidence confirms that such optimal power laws exist in many large-scale social systems that are constructed primarily via language-related interactions. This study contributes to the empirical explorations and theoretical discussions of the evolutionary relations between ubiquitous power laws in social systems and relevant individual behaviors.

Reflexive thought-ecologies: a reflection on practice

Purpose – The development of capacities for sustainability thinking by large-scale social systems requires of the skilled practitioner complex abstract logics. There is an intricate complexity of relationships to consider in the observation of the landscape of thought. This paper aims to introduce a matrix as an orientating heuristic to guide this form of praxis and provides reflections on its use in the enactment of social learning. Design/methodology/approach – The signifiers for four different orientations to learning and four abstractions in levels of learning are described. The configurations that result from their conjunction generate a matrix of 16 alternatives. These are combined into a heuristic to guide and inform reflexive praxis. Findings – The conjunction of these two dimensions enables observations of the cybernetic interactions between levels of learning and orientations to learning. This depiction prompts considerations of the ethics and aesthetics of large-scale collaborative learning. In reflecting on the use of the heuristic device in practice, four primary observations are offered as possible considerations to inform ethical practice. Originality/value – The value of this research is in enabling awareness of the relationship between levels of learning and orientations to learning. The originality is the application of apithology principles to the multi-dimensional learning landscapes found in complex thought-ecologies.

Contact-based social contagion in multiplex networks

We develop a theoretical framework for the study of epidemiclike social contagion in large scale social systems. We consider the most general setting in which different communication platforms or categories form multiplex networks. Specifically, we propose a contact-based information spreading model, and show that the critical point of the multiplex system associated with the active phase is determined by the layer whose contact probability matrix has the largest eigenvalue. The framework is applied to a number of different situations, including a real multiplex system. Finally, we also show that when the system through which information is disseminating is inherently multiplex, working with the graph that results from the aggregation of the different layers is inaccurate.

Social influence and spread dynamics in social networks.

Social networks often serve as a critical medium for information dissemination, diffusion of epidemics, and spread of behavior, by shared activities or similarities between individuals. Recently, we have witnessed an explosion of interest in studying social influence and spread dynamics in social networks. To date, relatively little material has been provided on a comprehensive review in this field. This brief survey addresses this issue. We present the current significant empirical studies on real social systems, including network construction methods, measures of network, and newly empirical results. We then provide a concise description of some related social models from both macro- and micro-level perspectives. Due to the difficulties in combining real data and simulation data for verifying and validating real social systems, we further emphasize the current research results of computational experiments. We hope this paper can provide researchers significant insights into better understanding the characteristics of personal influence and spread patterns in large-scale social systems.