Suchergebnisse

Abstract: One of the challenges of monitoring political mobilization on social media is the setting of formalized mobilization criteria to identify early symptoms of political mobilization across different groups. We propose and test a tool for identifying groups that display a high level of political mobilization. This tool is based on the hypothesis that strong engagement of users in group activities and discussions is common to groups in a state of self-organized criticality (SOC). An attribute of SOC, known as pink noise, can be observed in time series. To test this hypothesis, we sourced data on activity in 200 Facebook groups in France and Germany.Keywords: self-organized criticality, pink noise, social media, political mobilization***La théorie de la criticité auto-organisée aide-t-elle à identifier la mobilisation politique sur les réseaux sociaux ?Résumé: L'une des difficultés de la surveillance de la mobilisation politique sur les réseaux sociaux consiste en la définition des critères de formalisation de ces mouvements afin d'identifier les premiers symptômes de mobilisation politique sur les réseaux sociaux. Nous proposons et testons un outil capable d'identifier des groupes qui affichent un fort taux de mobilisation politique. Cet outil repose sur l'hypothèse selon laquelle un fort niveau d'engagement des utilisateurs dans les activités et discussions du groupe est commun aux groupes dans un état de criticité auto-organisée (SOC). L'une des particularités de la SOC, connue sous le nom de pink noise (bruit rose), peut être observée dans les séries temporelles. Afin de tester cette hypothèse, nous avons récolté des statistiques dans 200 groupes Facebook en France et en Allemagne.Mots-clés: criticité auto-organisée, bruit rose, réseaux sociaux, mobilisation politique

"This article is dedicated to application of theory and methodology of fractal geometry in historical research. The article represents the concrete historic issue mathematical model, specifically: the dynamics of the conscience and social environment modernization. On the basis of this model a computer program, which generates fractal images of attractors, attractor basins, and phase transformations of the social systems studied subject to user-entered numerical indicators of certain factors, has been developed. The article represents the principal approaches to the qualitative interpretation of the fractal images obtained." (author's abstract)

"The article presents several results of the computer modeling of demographic processes in the late traditional rural communities by means of fractal geometry. A team of contributors developed a model and software for it, then carried out its verification, data processing, computer modeling and Interpretation of results. The analysis of modeling outcomes allowed to build a holistic picture of the demographic behavior in rural communities of the Tambov province - one of the typical agrarian regions of 19th and 20th century Russia. Authors describe the degree and ways of how demographic behavior of the society was influenced by such factors as famine, war, epidemics, a level of health care infrastructure development, etc. Besides, it was possible to trace some non-linear effects in demographic strategies agrarian communities followed during modernization processes in Russia in the second half of the 19th and 20th centuries." (author's abstract)

This paper introduces an innovative social process model addressing population-wide measures of voter preferences that was tested on data from the 2016 US presidential election. Population-wide, "macroscopic" parameters are needed when privacy, ethics or regulatory constraints block "big data" techniques (e.g., in political contexts to counter "micro-targeting"). Confidence will be eroded if existing trend models and other macroscopic approaches frequently fail to predict outcomes, however campaign data reveal mathematical features that suggest a different possible approach. Given that the populations modelled exhibit self-organisation and memory when transmitting viewpoints, our model is based on mathematical representations of such processes. Its validation indicates the applicability and potential generalisability of this theoretical approach. In order to design a stochastic dynamics model of changing voter preferences, we evaluated probability models for transitions between possible system states (magnitudes of voter preferences), formulated the boundary task for probability density functions and derived a second-order non-linear differential equation incorporating self-organisation and memory. We find consistent dependencies between influences on the system and its reaction, and it is congruent with empirical data. The ability to use researchable global parameters indicates the potential for modelling electoral processes and wider applicability for complex social processes, avoiding dependence on "internal" variables. ; Post-print / Final draft

This conceptual research presents a new stochastic model of the dynamics of state-to-state transitions in social systems, the Zhukov–Khvatova model. Employing a mathematical approach based on percolation theory the model caters for random changes, system memory and self-organisation. Curves representing the approach of the system to the percolation threshold differ significantly from the smooth S-shaped curves predicted by existing models, showing oscillations, steps and abrupt steep gradients. The modelling approach is new, working with system level parameters, avoiding reference to node-level changes and modelling a non-Markov process by including self-organisation and the effects (memory) of previous system states over a configurable number of time intervals. Computational modelling is used to demonstrate how the percolation threshold (i.e. the share of nodes which allows information to spread freely within the network) is reached. Possible applications of the model discussed include modelling the dynamics of viewpoints in society during social unrest and elections, changing attitudes in social networks and forecasting the outcome of promotions or uptake of campaigns. The easy availability of system level data (network connectivity, evolving system penetration) makes the model a particularly valuable addition to the toolkit for social sciences, politics, and potentially marketing. ; Post-print / Final draft

This study focused on a Russian agrarian microregion (12.5 km2) situated in a forest steppe area (in Tambov Oblast) during the 18th–19th centuries. The research illuminates the mechanisms, patterns, and impact that the environment has on social processes, and vice versa — the human impact on the environment (and particularly on landscape). The authors created a geographic information system which they used to compare the results of aerial survey and satellite imagery, with historical maps and other sources, including archival documents. Morphological anomalies were identified on an orthophotomap, and then interpreted. The conclusions of the research concern the nature of interaction between the agrarian society and the environment. Settlement in the plains of the forest steppe was heavily dependent on the water regime, which directly affected the condition and dynamics of communication routes and economic infrastructure. The vanished settlement found in this study is an example of such an effect: it grew up along a road and was abandoned when the areas through which the road ran were waterlogged. Plain rivers can alter the surrounding landscape quite drastically over relatively short historical periods, and are themselves subject to anthropogenic influence. Changes in living conditions and economic management led to variability in settlement patterns and land use. This can be seen especially well in the lowland, where such schemes were predominantly temporary. The processing of Earth remote sensing data makes it possible to fill gaps in sources and discover new evidence of the past.