Social Collective Behavior Research Articles

Collective Identity and Ethical Dilemmas Among College Students in Chinese Anonymous Online Communities: Sensory Stimulation and Media Effects of Online Exposure Discourse

This paper explores the dynamics of viral social media exposure posts within anonymous online communities in China, specifically concentrating on college students. Based on theories of effervescence and collective frenzy, this research probes into the emotional triggers and functions of group polarization and social identity reconstruction in the digital sphere. Adopting a mixed-methods approach that integrates semi-structured interviews and surveys, it evaluates participants' interactions with online exposure. The anticipated findings are expected to shed light on how factors like group identity, anonymity, and virality cultivate a carnival-like ambiance in social media exposure. These posts function as instruments for mobilization, provide support for social and political causes, and act as platforms for community building and identity formation. They also offer psychological and emotional outlets, aiding users in processing and expressing personal and societal grievances. The paper aims to deepen people's understanding of how digital interactions influence collective identities and social behaviors.

Depiction of Woman in Heer Waris Shah

In every folktale characters hold a significant place because they are not confined to describing or fantasizing the love relations but they reflect the collective social behaviors of the concerned society. Waris Shah portrays Punjabi culture in his classic Heer Ranjha and elaborates on the internal social system and every character of the tale represents the core realities of society. The present study is to highlight the intellectual, symbolic, social, and cultural representation of female characters in Heer Waris Shah.

Neural basis of collective social behavior during environmental challenge.

Humans and animals have a remarkable capacity to collectively coordinate their behavior to respond to environmental challenges. However, the underlying neurobiology remains poorly understood. Here, we found that groups of mice self-organize into huddles at cold ambient temperature during the thermal challenge assay. We found that mice make active (self-initiated) and passive (partner-initiated) decisions to enter or exit a huddle. Using microendoscopic calcium imaging, we found that active and passive decisions are encoded distinctly within the dorsomedial prefrontal cortex (dmPFC). Silencing dmPFC activity in some mice reduced their active decision-making, but also induced a compensatory increase in active decisions by non-manipulated partners, conserving the group's overall huddle time. These findings reveal how collective behavior is implemented in neurobiological mechanisms to meet homeostatic needs during environmental challenges.

Who Controls the National Academic Promotion System: An Analysis of Power Distribution in Poland

In studying the performance of academic systems, one often tries to find out—by inspecting some well-defined parameters—how specific changes in the legal system influence the volume and quality of desired outputs, such as publications, patents, or alumni. This mechanistic approach neglects the fact that the main actors in this spectacle are not passive, but have their own priorities, and actively self-organize in response to any changes in their professional environment. In this contribution, we investigate such collective social behavior using the data for academic promotions in Poland in the years 2011 to 2020. Focusing on degrees higher than the PhD, we analyzed over 12,500 habilitation (= higher doctorate) and over 3,000 professorship applications, extracting personal data on academic promotion panel membership along with official roles on the panels. These details were then linked with data on the personal make-up of three consecutive terms of the central body overseeing academic promotions in Poland, Centralna Komisja do Spraw Stopni i Tytułów (“Central Board for Degrees and Titles”). As a result, we were able to identify a privileged group of individuals who controlled a substantial majority of academic promotions beyond the PhD degree by virtue of being part of the Central Board. We found that this relatively small group of academics held dominant control over academic promotions by repeatedly serving on promotion panels. Thus, despite the well-meaning intentions of the reformists who had designed it, the Polish system of academic promotions in the past decade can hardly be regarded as based on pure merit.

Utilizing statistical physics and machine learning to discover collective behavior on temporal social networks

Computational social science has become a branch of social science that uses computationally intensive ways to investigate and model social phenomena. Exploitation on mathematics, physics, and computer sciences, and analytic approaches like Social Network Analysis (SNA), Machine Learning (ML), etc, develops and tests the theories of complex social phenomena. In the emerging environment of social media, the new characteristics of social collective behavior and its extensive phenomena have become the hot spot of common concern across many disciplines. In this paper, we propose a general quantitative framework to discover the social collective behavior in temporal social networks. The general framework incorporates the Time-Correlation Function (T.C.F.) in statistical physics and evolutionary approach in Machine Learning, and provides the quantitative evidence of the existence of social collective behavior. Results show collective behaviors are observed and there exists a tiny fraction of users whose behavior are constantly replicated by public, disregard of the behavior itself. Our method is assumption-independent and has the potential to be applied to various temporal systems.

Cannot Predict Comment Volume of a News Article before (a few) Users Read It

Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, i.e., comments. An article may attract an apathetic user engagement (several tens of comments) or a spontaneous fervent user engagement (thousands of comments). In this paper, we study the problem of predicting the total number of user comments a news article will receive. Our main insight is that user-to-user interaction factors contribute the most to an accurate prediction, while news article specific factors have surprisingly little influence. This appears to be an interesting and understudied phenomenon: collective social behavior at a news outlet shapes user response and may even downplay the content of an article. We compile and analyze a large number of features, both old and novel from literature. The features span a broad spectrum of facets including news article and comment contents, temporal dynamics, sentiment/linguistic features, and user behaviors. We show that the early arrival rate of comments is the best indicator of the eventual number of comments. We conduct an in-depth analysis of this feature across several dimensions, such as news outlets and news article categories. We show that the relationship between the early rate and the final number of comments as well as the prediction accuracy vary considerably across news outlets and news article categories (e.g., politics, sports, or health).

Analyzing mass media influence using natural language processing and time series analysis

A key question of collective social behavior is related to the influence of mass media on public opinion. Different approaches have been developed to address quantitatively this issue, ranging from field experiments to mathematical models. In this work we propose a combination of tools involving natural language processing and time series analysis. We compare selected features of mass media news articles with measurable manifestation of public opinion. We apply our analysis to news articles belonging to the 2016 US presidential campaign. We compare variations in polls (as a proxy of public opinion) with changes in the connotation of the news (sentiment) or in the agenda (topics) of a selected group of media outlets. Our results suggest that the sentiment content by itself is not enough to understand the differences in polls, but the combination of topics coverage and sentiment content provides an useful insight of the context in which public opinion varies. The methodology employed in this work is far general and can be easily extended to other topics of interest.

Marketing analysis of wineries using social collective behavior from users’ temporal activity on Twitter

Marketing professionals face challenges of increasing complexity to adapt classic marketing strategies to the phenomenon of social networks. Companies are currently trying to take advantage of the useful collective knowledge available on social networks to support different types of marketing decisions. The appropriate analysis of this information can offer marketing professionals with important competitive advantages. This work proposes a new methodology to extract the social collective behavior of Twitter users concerning a group of brands based on the users’ temporal activity. Time series of mentions made by individual users to each company’s Twitter account are aggregated to obtain collective activity data for the companies, which is a consequence of both the company’s and other users’ actions. These data are processed using classical unsupervised machine learning techniques, such as temporal clustering and hidden Markov models, to extract collective temporal behavior patterns and models of the dynamics of customers over time for a single brand and groups of brands. The derived knowledge can be used for different tasks, such as identifying the impact of a marketing campaign on Twitter and comparatively assessing the social behaviors of different brands and groups of brands to assist in making marketing decisions. Our methodology is validated in a case study from the wine market. Twitter data were gathered from four regions of different countries around the world with important wineries (Italy: Veneto, Portugal: Porto and Douro Valley, Spain: La Rioja, and United States: Napa Valley), and comparative behavior analysis was carried out from the perspective of the use of Twitter as a communication channel for marketing campaigns.

The dynamics of collective social behavior in a crowd controlled game

Despite many efforts, the behavior of a crowd is not fully understood. The advent of modern communication means has made it an even more challenging problem, as crowd dynamics could be driven by both human-to-human and human-technology interactions. Here, we study the dynamics of a crowd controlled game (Twitch Plays Pokémon), in which nearly a million players participated during more than two weeks. Unlike other online games, in this event all the players controlled exactly the same character and thus it represents an exceptional example of a collective mind working to achieve a certain goal. We dissect the temporal evolution of the system dynamics along the two distinct phases that characterized the game. We find that having a fraction of players who do not follow the crowd’s average behavior is key to succeed in the game. The latter finding can be well explained by an nth order Markov model that reproduces the observed behavior. Secondly, we analyze a phase of the game in which players were able to decide between two different modes of playing, mimicking a voting system. We show that the introduction of this system clearly polarized the community, splitting it in two. Finally, we discuss one of the peculiarities of these groups in the light of the social identity theory, which appears to describe well some of the observed dynamics.

Reality-inspired voter models: A mini-review

This mini-review presents extensions of the voter model that incorporate various plausible features of real decision-making processes by individuals. Although these generalizations are not calibrated by empirical data, the resulting dynamics are suggestive of realistic collective social behaviors. Cette mini-revue présente des extensions du modèle de l'électeur qui intègrent divers éléments plausibles des processus réels de prise de décision par des individus. Bien que ces généralisations ne soient pas calibrées par des données empiriques, la dynamique qui en résulte suggère des comportements sociaux collectifs réalistes.

Modeling and intelligent optimization of social collective behavior with online public opinion synchronization

Today, the public opinion synchronization on the network platform is becoming one of important issues worthy of careful study. In this paper, we take synchronization evolution phenomenon as an objective and adopt artificial bee colony (ABC) to evaluate network synchronization effects with optimization theory to find out an appropriate network structure. Firstly, we use the Kuramoto oscillators as a metaphor of the social system collective behavior. Secondly, combined with the social network characteristics obtained from the data of Sina Micro-Blog, a synchronization evolution model of Internet public opinion based on Kuramoto one is established. Subsequently, evolutionary multi-objective optimization model is set up and the ABC method is used to optimize the level of network synchronization, the synchronization starting time and the cost of public opinion synchronization. Finally, case analysis on “Double Eleven” Internet Marketing as well as Cadmium Poisoned Rice Event demonstrates that the synchronization performance of weak coupled system can be enhanced by offering reasonable configuration of the connection cost and the synchronization duration cost. In addition, a certain degree of increase in input cost can promote the synchronization performance and extend the synchronization duration significantly.

Sociophysics

To the extent that individuals interact with each other in prescribed ways, their collective social behavior can be modeled and analyzed.

Stationary ensembles in threshold networks

The networks of threshold elements are considered. The notion of a stationary ensemble is introduced. We set out necessary and sufficient conditions for a network to be an ensemble. We show that for two ensembles with common elements switching on one ensemble does not necessarily lead to switching on the other. A representation of an ensemble as a finite state machine is proposed. We then show how this representation can help study the processes of switching ensembles on and off. We note that a network of ensembles may be interpreted in neurobiological terms as a basis for a long-term memory model. In social sciences it may serve as a network version of the collective social threshold behavior model.

Designs for Everyone: Transforming Women’s Fashions in Early Twentieth-Century Russia

The world of women’s fashion in early twentieth-century Russia provides a rich context for measuring shifts in class identity and in gender norms, as the major cities of Moscow and St. Petersburg were witnessing broad social transformation. If not for the Revolution, the late-Imperial period may well have anticipated the mature markets of the West, where haute couture and the garment industry fueled widespread consumption and became what are now essential components of modern collective social behavior. In Russia, the intensified urbanization of the early twentieth century also ushered in the rise of new forms of popular culture, which often intersected with the world of women’s fashion. Specialized periodicals, such as fashion magazines and the new art of cinema, fueled a cult interest in the latest sartorial trends. A reflection of this phenomenon can also be found in Teffi’s (pseudonym of Nadezhda Aleksandrovna Lokhvitskaia, 1872-1953) broadly circulated stories, which allowed readers to better understand the perceived transformative power of fashion, even when expressed on the seemingly minor level of a small collar or hat.

Why Wisdom of the Crowd has Predictive Power for the Financial Markets?

Recent reports quantifying collective behavior through Twitter moods, Google queries, Wikipedia patterns, etc., have attracted much attention for their incredible predictive power for the financial market outcomes. Why such wisdom of the crowd beats the unpredictability of markets is relatively less understood. In the present study, we advance an explanation towards this puzzle by drawing an analogy between stock market and the human brain from the behavior of fluctuations and the nature of synchronization in these seemingly different systems, with concrete empirical evidence from the financial crisis of 2008. Akin to the neuronal circuitry in brain, we identify that the synchronization between companies belonging to the same and distinct business sector manifest in well-separated high (gamma) and low (theta) oscillation frequency modes, respectively. The quantitative analysis of the stock market behavior is carried out using complex networks, in a random matrix framework. Considering the well-separated scales of fluctuations, we take recourse to ‘Wavelet transform’ for disentangling these non-stationary variations. As the long-range synchronization in the human brain can predict the forthcoming stimuli by top-down mechanism, adaptively matching ‘memory’ driven expectations against signals from environment, “early warning signatures” about dynamics of financial markets may probably be predicted from the collective social behavior in the same way. The brain-market analogy is possibly rooted in attributes of certain scale-invariant complex network.

Alteridad y oportunidad: discurso y migración

Objetivos: el presente artículo argumenta en favor de una conceptualización heteroglósica del fenómeno migratorio. Con apoyo en resultados de un estudio discursivo con migrantes peruanas en Lima, Perú, elabora una metodología de análisis que se interesa tanto por la sujeción de los migrantes como por su subjetivación individual y colectiva por medio de la migración en un mundo globalizado.Materiales y método: se basa en el establecimiento del diálogo con los migrantes, es decir, en la legitimación de sus voces como participantes en la construcción social de la migración como fenómeno social. Con base en el concepto del discurso y su análisis crítico, elabora los recursos simbólicos diversos e híbridos a los que recurren los migrantes al construir la realidad social de la que forman parte.Resultados: al abrirse a la diferencia cultural del otro migrante mediante la metodología propuesta, se logra entender la realidad social que construyen los migrantes en, con y por la migración. El encuentro con la diversidad de sus voces demuestra las oportunidades que residen en la alteridad. Sus voces permiten no solamente acercarse conceptualmente al fenómeno social de la migración, sino que también dan una idea de las posibilidades de agencia individual y colectiva social en un mundo posmoderno globalizado.Conclusiones: el encuentro con las voces de los migrantes, tal como lo permite el análisis del discurso propuesto, puede promover la conciencia de las condiciones de existencia en el mundo moderno y sensibilizar en miras a la discursividad de la realidad social en general.

Evolutionary Dynamics of Collective Behavior Selection and Drift: Flocking, Collapse, and Oscillation.

Behavioral choice is ubiquitous across a wide range of interactive decision-making processes and a myriad of scientific disciplines. With regard to this issue, one entitative problem is actually to understand how collective social behaviors form and evolve among populations when they face a variety of conflict alternatives. In this paper, a selection-drift dynamic model is formulated to characterize the behavior imitation and exploration processes in social populations. Based on the proposed framework, several typical behavior evolution patterns, including behavioral flocking, collapse, and oscillation, are reproduced with different kinds of behavior networks. Interestingly, for the selection-drift dynamics on homogeneous symmetric behavior networks, we unveil the phase transition from behavioral flocking to collapse and derive the bifurcation diagram of the evolutionary stable behaviors in social behavior evolution. While via analyzing the survival conditions of the best behavior on heterogeneous symmetric behavior networks, we propose a selection-drift mechanism to guarantee consensus at the optimal behavior. Moreover, when the selection-drift dynamics on asymmetric behavior networks is simulated, it is shown that breaking the symmetry in behavior networks can induce various behavioral oscillations. These obtained results may shed new insights into understanding, detecting, and further controlling how social norm and cultural trends evolve.

Leader–follower model for agent based simulation of social collective behavior during egress

There is growing consensus that individuals egressing during emergencies engage in social collective behavior that is ordered and cooperative. Despite this fact however, a great many of the existing models either do not account for this type of behavior or model it in an inadequate manner. In this paper, a leader–follower agent-based model is proposed to interpret local social interactions and collective behavior and then use this information to mimic three particular scenarios: lining up in counter-flow, queuing, and collective mobility. To achieve this, a pedestrian agent can establish informal and transient leader–follower relationships with others while adjusting its behavioral patterns as warranted by the situation. The proposed model is calibrated to existing field data and then validated using another set of field data, where it is shown that the new model is capable of reasonably simulating social collective behavior during egress.

Timescales of massive human entrainment.

The past two decades have seen an upsurge of interest in the collective behaviors of complex systems composed of many agents entrained to each other and to external events. In this paper, we extend the concept of entrainment to the dynamics of human collective attention. We conducted a detailed investigation of the unfolding of human entrainment—as expressed by the content and patterns of hundreds of thousands of messages on Twitter—during the 2012 US presidential debates. By time-locking these data sources, we quantify the impact of the unfolding debate on human attention at three time scales. We show that collective social behavior covaries second-by-second to the interactional dynamics of the debates: A candidate speaking induces rapid increases in mentions of his name on social media and decreases in mentions of the other candidate. Moreover, interruptions by an interlocutor increase the attention received. We also highlight a distinct time scale for the impact of salient content during the debates: Across well-known remarks in each debate, mentions in social media start within 5–10 seconds after it occurs; peak at approximately one minute; and slowly decay in a consistent fashion across well-known events during the debates. Finally, we show that public attention after an initial burst slowly decays through the course of the debates. Thus we demonstrate that large-scale human entrainment may hold across a number of distinct scales, in an exquisitely time-locked fashion. The methods and results pave the way for careful study of the dynamics and mechanisms of large-scale human entrainment.

Opinion formation models on a gradient.

Statistical physicists have become interested in models of collective social behavior such as opinion formation, where individuals change their inherently preferred opinion if their friends disagree. Real preferences often depend on regional cultural differences, which we model here as a spatial gradient g in the initial opinion. The gradient does not only add reality to the model. It can also reveal that opinion clusters in two dimensions are typically in the standard (i.e., independent) percolation universality class, thus settling a recent controversy about a non-consensus model. However, using analytical and numerical tools, we also present a model where the width of the transition between opinions scales , not as in independent percolation, and the cluster size distribution is consistent with first-order percolation.