Journal articles: 'Social entropy'

1

Huet, Marie-Helene. "Social Entropy." Yale French Studies, no. 92 (1997): 171. http://dx.doi.org/10.2307/2930392.

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Firebaugh, Glenn, and Kenneth D. Bailey. "Social Entropy Theory." Contemporary Sociology 20, no. 1 (January 1991): 151. http://dx.doi.org/10.2307/2072160.

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Zhang, Zhanli. "Coupling entropy of co-processing model on social networks." Modern Physics Letters B 29, no. 25 (September 20, 2015): 1550149. http://dx.doi.org/10.1142/s0217984915501493.

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Coupling entropy of co-processing model on social networks is investigated in this paper. As one crucial factor to determine the processing ability of nodes, the information flow with potential time lag is modeled by co-processing diffusion which couples the continuous time processing and the discrete diffusing dynamics. Exact results on master equation and stationary state are achieved to disclose the formation. In order to understand the evolution of the co-processing and design the optimal routing strategy according to the maximal entropic diffusion on networks, we propose the coupling entropy comprehending the structural characteristics and information propagation on social network. Based on the analysis of the co-processing model, we analyze the coupling impact of the structural factor and information propagating factor on the coupling entropy, where the analytical results fit well with the numerical ones on scale-free social networks.

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Dinga, Emil, Cristina-Roxana Tănăsescu, and Gabriela-Mariana Ionescu. "Social Entropy and Normative Network." Entropy 22, no. 9 (September 20, 2020): 1051. http://dx.doi.org/10.3390/e22091051.

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The paper introduces a new concept of social entropy and a new concept of social order, both based on the normative framework of society. From these two concepts, typologies (logical and historical) of societies are inferred and examined in their basic features. To these ends, some well-known concepts such as entropy, order, system, network, synergy, norm, autopoieticity, fetality, and complexity are revisited and placed into an integrated framework. The core body of this paper addresses the structure and the mechanism of social entropy, understood as an institutionally working counterpart of social order. Finally, this paper concludes that social entropy is an artefact, like society itself, and acts through people’s behavior.

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No authorship indicated. "Review of Social Entropy Theory." Contemporary Psychology: A Journal of Reviews 36, no. 4 (April 1991): 347. http://dx.doi.org/10.1037/029668.

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Mayer, Thomas F. "Social Entropy Theory.Kenneth D. Bailey." American Journal of Sociology 96, no. 6 (May 1991): 1544–46. http://dx.doi.org/10.1086/229700.

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Zhao, Kun, Márton Karsai, and Ginestra Bianconi. "Entropy of Dynamical Social Networks." PLoS ONE 6, no. 12 (December 16, 2011): e28116. http://dx.doi.org/10.1371/journal.pone.0028116.

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Bailey, Kenneth D. "Sociocybernetics and social entropy theory." Kybernetes 35, no. 3/4 (March 2006): 375–84. http://dx.doi.org/10.1108/03684920610653683.

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Bailey, K. D. "Social Entropy Theory: An overview." Systems Practice 3, no. 4 (August 1990): 365–82. http://dx.doi.org/10.1007/bf01063441.

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Sarna, Geetika, and M. P. S. Bhatia. "Entropy Based Identification of Fake Profiles in Social Network." International Journal of Virtual Communities and Social Networking 9, no. 4 (October 2017): 18–30. http://dx.doi.org/10.4018/ijvcsn.2017100102.

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Cyberbullying is a felonious act carried out against the victim by sending harassing/ embarrassing/ abusing information online. Normally offenders create fake profiles in order to hide their identity for unscrupulous activities. Assuming a fake identity is very harmful as the real picture of the offender is not visible, and also it can become difficult to entrap them. Sometimes, some trustworthy friends can also take advantage of the fake identity in order to harm the victim. Culprits can reveal victim's personal information like financial details, personal history, family, etc., and along with it, he can harass, threaten or blackmail the victim using fake profiles and permeates that information on the social network. So, it is necessary to resolve this issue. In this article, the authors used the concept of entropy and cross entropy to identify fake profiles as entropy works on the degree of uncertainty. Also, this article shows the comparison of proposed method with the existing classifiers.

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Swanson, G. A., Kenneth D. Bailey, and James Grier Miller. "Entropy, Social Entropy and Money: A Living Systems Theory Perspective." Systems Research and Behavioral Science 14, no. 1 (January 1997): 45–65. http://dx.doi.org/10.1002/(sici)1099-1743(199701/02)14:1<45::aid-sres151>3.0.co;2-y.

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Stepanić, Josip, Gabrijela Sabol, and Mislav Stjepan Žebec. "Describing social systems using social free energy and social entropy." Kybernetes 34, no. 6 (July 2005): 857–68. http://dx.doi.org/10.1108/03684920510595535.

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Larsen, Erik. "Entropy in the Circuits." Nineteenth-Century Literature 69, no. 4 (March 1, 2015): 509–38. http://dx.doi.org/10.1525/ncl.2015.69.4.509.

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Erik Larsen, “Entropy in the Circuits: McTeague’s Apocalyptic Posthumanism” (pp. 509–538) This essay reinterprets Frank Norris’s novel McTeague: A Story of San Francisco (1899) as a depiction of the annihilating effects of entropy on human and material systems. Focusing on McTeague’s lengthy and underanalyzed conclusion, in which McTeague flees into the heart of Death Valley, I argue that Norris’s descriptions of the desert identify an irresistible and destructive force guiding the disintegration of individuals, relationships, and ultimately the Earth itself. Drawing on the record of cultural anxieties surrounding the laws of thermodynamics in the nineteenth century, the essay demonstrates how McTeague exemplifies an “apocalyptic posthumanism” with implications far more disruptive to human exceptionalism than those of traditional biological determinism. The essay also interprets social, biological, and material systems in the novel as attempting, unsuccessfully, to resist entropic decline by channeling and diversifying forces through systems resembling electrical circuits. In this context, gold is read as the “current” or “currency” subtending California’s economic and social worlds, but also that which drives them to greater and greater states of entropic disorder and eventual collapse.

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Russell, J. S. "Striving, entropy, and meaning." Journal of the Philosophy of Sport 47, no. 3 (July 6, 2020): 419–37. http://dx.doi.org/10.1080/00948705.2020.1789987.

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Dobrescu, Smaranda, and Radu Dobrescu. "Social Entropy, Technological Change and International Stability." IFAC Proceedings Volumes 31, no. 6 (May 1998): 21–28. http://dx.doi.org/10.1016/s1474-6670(17)40283-7.

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Jena, Ratiranjan, and Biswajeet Pradhan. "Earthquake Social Vulnerability Assessment Using Entropy Method." IOP Conference Series: Earth and Environmental Science 540 (August 5, 2020): 012079. http://dx.doi.org/10.1088/1755-1315/540/1/012079.

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Bailey, Kenneth D. "Living systems theory and social entropy theory." Systems Research and Behavioral Science 23, no. 3 (May 23, 2006): 291–300. http://dx.doi.org/10.1002/sres.728.

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Stahl, D. O. "Entropy control costs and entropic equilibria." International Journal of Game Theory 19, no. 2 (June 1990): 129–38. http://dx.doi.org/10.1007/bf01761072.

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Gullifer, Jason W., and Debra Titone. "Characterizing the social diversity of bilingualism using language entropy." Bilingualism: Language and Cognition 23, no. 2 (March 5, 2019): 283–94. http://dx.doi.org/10.1017/s1366728919000026.

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AbstractBilingual and multilingual individuals exhibit variation in everyday language experience. Studies on bilingualism account for individual differences with measures such as L2 age of acquisition, exposure, or language proficiency, but recent theoretical perspectives posit that the relative balance between the two or more languages throughout daily life (i.e., interactional context) is a crucial determinant for language representation, access, and control. We propose an innovative measure to characterize this construct by using entropy to estimate the social diversity of language use. Language entropy is computed from commonly-collected language history data and generalizes to multilingual communicative contexts. We show how language entropy relates to other indices of bilingual experience and that it predicts self-report L2 outcome measures over and above classic measures of language experience. Thus, we proffer language entropy as a means to characterize individual differences in bilingual (and multilingual) language experience related to the social diversity of language use.

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Matei, Sorin Adam, and Robert J. Bruno. "Pareto's 80/20 law and social differentiation: A social entropy perspective." Public Relations Review 41, no. 2 (June 2015): 178–86. http://dx.doi.org/10.1016/j.pubrev.2014.11.006.

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Mann, Richard P., and Roman Garnett. "The entropic basis of collective behaviour." Journal of The Royal Society Interface 12, no. 106 (May 2015): 20150037. http://dx.doi.org/10.1098/rsif.2015.0037.

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We identify a unique viewpoint on the collective behaviour of intelligent agents. We first develop a highly general abstract model for the possible future lives these agents may encounter as a result of their decisions. In the context of these possibilities, we show that the causal entropic principle , whereby agents follow behavioural rules that maximize their entropy over all paths through the future, predicts many of the observed features of social interactions among both human and animal groups. Our results indicate that agents are often able to maximize their future path entropy by remaining cohesive as a group and that this cohesion leads to collectively intelligent outcomes that depend strongly on the distribution of the number of possible future paths. We derive social interaction rules that are consistent with maximum entropy group behaviour for both discrete and continuous decision spaces. Our analysis further predicts that social interactions are likely to be fundamentally based on Weber's law of response to proportional stimuli, supporting many studies that find a neurological basis for this stimulus–response mechanism and providing a novel basis for the common assumption of linearly additive ‘social forces’ in simulation studies of collective behaviour.

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Mavrofides, Thomas, Achilleas Kameas, Dimitris Papageorgiou, and Antonios Los. "On the Entropy of Social Systems: A Revision of the Concepts of Entropy and Energy in the Social Context." Systems Research and Behavioral Science 28, no. 4 (March 2, 2011): 353–68. http://dx.doi.org/10.1002/sres.1084.

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Tsallis, Constantino. "Entropy." Encyclopedia 2, no. 1 (January 28, 2022): 264–300. http://dx.doi.org/10.3390/encyclopedia2010018.

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The concept of entropy constitutes, together with energy, a cornerstone of contemporary physics and related areas. It was originally introduced by Clausius in 1865 along abstract lines focusing on thermodynamical irreversibility of macroscopic physical processes. In the next decade, Boltzmann made the genius connection—further developed by Gibbs—of the entropy with the microscopic world, which led to the formulation of a new and impressively successful physical theory, thereafter named statistical mechanics. The extension to quantum mechanical systems was formalized by von Neumann in 1927, and the connections with the theory of communications and, more widely, with the theory of information were respectively introduced by Shannon in 1948 and Jaynes in 1957. Since then, over fifty new entropic functionals emerged in the scientific and technological literature. The most popular among them are the additive Renyi one introduced in 1961, and the nonadditive one introduced in 1988 as a basis for the generalization of the Boltzmann–Gibbs and related equilibrium and nonequilibrium theories, focusing on natural, artificial and social complex systems. Along such lines, theoretical, experimental, observational and computational efforts, and their connections to nonlinear dynamical systems and the theory of probabilities, are currently under progress. Illustrative applications, in physics and elsewhere, of these recent developments are briefly described in the present synopsis.

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Koyama and Niwase. "An Entropy Analysis of Classroom Conditions Based on Mathematical Social Science." Education Sciences 9, no. 4 (December 6, 2019): 288. http://dx.doi.org/10.3390/educsci9040288.

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In classroom management, it is well-known that students’ mental states are strongly related to classroom conditions. There are many ways to describe human behavior in mathematical modeling in sociology. In social science, a model to describe human behavior has been developed by an analogy with the ferromagnetic spin model in statistical physics. Entropy, on the other hand, can express the order and/or disorder in many-body systems. The concept of entropy can be extended to continuous random variables in the information theory, which is called “differential entropy” and has been a powerful tool in many stochastic systems. Here, we show that classroom conditions can be expressed by the differential entropy, based on the model used in social science. To assess the applicability of this method to real classroom conditions, we investigated fluctuations in students’ minds with pictures and a questionnaire relating to school life, and then applied this to the present method, and calculated the differential entropy. The results correspond well with real classroom conditions, and suggest the usefulness of the present method. The significance of the present research is the proposal of a new method which has not yet been used in educational psychology.

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Li, Ke, Hui-Jia Li, and Hao Wang. "Situation analysis of relationship in social networks based on link entropy." Modern Physics Letters B 29, no. 13 (May 18, 2015): 1550061. http://dx.doi.org/10.1142/s021798491550061x.

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Since the existence of certain and uncertain characteristics of the relationships between nodes in social network, the study of social features is expanded by combining the set pair analysis and social computing. In this paper, a new method is created to describe nodes relationship situation in social network, i.e. set pair relationship situation, including generalized set pair relationship situation, generalized set pair close situation and generalized set pair loosen situation. In order to analyze the situation in social network, each kind of set pair relation situation are classified. Combining with the complexity of the social network system and the features of connection entropy, generalized connection entropy which used to express the complexity of social networks is proposed. It includes the generalized same entropy, the generalized difference entropy, and the generalized opposite entropy. These different types of entropies can be used to analyze the social network relationship stability from a more theoretical view. Then a situation analysis model and the corresponding algorithm is proposed. Finally the effectiveness of this method in analyzing the relationships in social networks is proved. Thus, our model can be used to reveal the relationship between social network and node state stability efficiently.

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Khrennikov, Andrei, and Noboru Watanabe. "Order-Stability in Complex Biological, Social, and AI-Systems from Quantum Information Theory." Entropy 23, no. 3 (March 16, 2021): 355. http://dx.doi.org/10.3390/e23030355.

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This paper is our attempt, on the basis of physical theory, to bring more clarification on the question “What is life?” formulated in the well-known book of Schrödinger in 1944. According to Schrödinger, the main distinguishing feature of a biosystem’s functioning is the ability to preserve its order structure or, in mathematical terms, to prevent increasing of entropy. However, Schrödinger’s analysis shows that the classical theory is not able to adequately describe the order-stability in a biosystem. Schrödinger also appealed to the ambiguous notion of negative entropy. We apply quantum theory. As is well-known, behaviour of the quantum von Neumann entropy crucially differs from behaviour of classical entropy. We consider a complex biosystem S composed of many subsystems, say proteins, cells, or neural networks in the brain, that is, S=(Si). We study the following problem: whether the compound system S can maintain “global order” in the situation of an increase of local disorder and if S can preserve the low entropy while other Si increase their entropies (may be essentially). We show that the entropy of a system as a whole can be constant, while the entropies of its parts rising. For classical systems, this is impossible, because the entropy of S cannot be less than the entropy of its subsystem Si. And if a subsystems’s entropy increases, then a system’s entropy should also increase, by at least the same amount. However, within the quantum information theory, the answer is positive. The significant role is played by the entanglement of a subsystems’ states. In the absence of entanglement, the increasing of local disorder implies an increasing disorder in the compound system S (as in the classical regime). In this note, we proceed within a quantum-like approach to mathematical modeling of information processing by biosystems—respecting the quantum laws need not be based on genuine quantum physical processes in biosystems. Recently, such modeling found numerous applications in molecular biology, genetics, evolution theory, cognition, psychology and decision making. The quantum-like model of order stability can be applied not only in biology, but also in social science and artificial intelligence.

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Zingg, Christian, Giona Casiraghi, Giacomo Vaccario, and Frank Schweitzer. "What Is the Entropy of a Social Organization?" Entropy 21, no. 9 (September 17, 2019): 901. http://dx.doi.org/10.3390/e21090901.

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We quantify a social organization’s potentiality, that is, its ability to attain different configurations. The organization is represented as a network in which nodes correspond to individuals and (multi-)edges to their multiple interactions. Attainable configurations are treated as realizations from a network ensemble. To have the ability to encode interaction preferences, we choose the generalized hypergeometric ensemble of random graphs, which is described by a closed-form probability distribution. From this distribution we calculate Shannon entropy as a measure of potentiality. This allows us to compare different organizations as well as different stages in the development of a given organization. The feasibility of the approach is demonstrated using data from three empirical and two synthetic systems.

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Proops, John L. R. "Entropy, Information and Confusion in the Social Sciences." Journal of Interdisciplinary Economics 1, no. 4 (January 1987): 225–42. http://dx.doi.org/10.1177/02601079x8700100403.

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The term “entropy” is now widely used in social science, although its origin is in physical science. There are three main ways in which the term may be used. The first invokes the original meaning, referring to the unidirectionality of heat flow, from hot bodies to cold ones. The second meaning can be derived from the first via statistical mechanics; this meaning is concerned with measures of ‘evenness’ of ‘similarity’. The third meaning derives from information theory. The three distinct meanings are carefully described and distinguished, and their relationships to each other are discussed. The various uses of the three concepts in the social sciences are then reviewed, including some uses which confuse the different meanings of the term. Finally, modern work in thermodynamics is examined, and its implications for economic analysis are briefly assessed.

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Rizescu, Diana, and Vasile Avram. "Using Onicescu's Informational Energy to Approximate Social Entropy." Procedia - Social and Behavioral Sciences 114 (February 2014): 377–81. http://dx.doi.org/10.1016/j.sbspro.2013.12.715.

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Starkermann, R. "Social entropy, enthalpy, exergy and disergy in examples." Mathematical and Computer Modelling 10, no. 6 (1988): 409–18. http://dx.doi.org/10.1016/0895-7177(88)90030-1.

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Rolínek, L., and M. Doktorová. "External and internal entropy assessment on farms in relation to their competitiveness." Agricultural Economics (Zemědělská ekonomika) 48, No. 2 (February 29, 2012): 61–64. http://dx.doi.org/10.17221/5289-agricecon.

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Internal and external entropy are indicators of evaluation of the success of the firm management. The evaluation of the file of the chosen agricultural firms shows, that the level of internal and external entropy is not too high for the future dynamics and development. Competitiveness of the evaluated firms can be influenced especially by problematic level of their interior social situation (it means social subsystem measured with the help of the internal entropy) that is connected with a not very efficient management of the human resources.

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Vetromille-Castro, Rafael. "Social interactive entropy and interaction in the language teacher education classroom." Revista Brasileira de Linguística Aplicada 13, no. 2 (June 2013): 625–41. http://dx.doi.org/10.1590/s1984-63982013000200012.

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This paper has as core ideas the assumption that interaction is essential for knowledge construction and the claim that groups of individuals in learning contexts can be seen as complex adaptive systems (CAS). Some different, but congruous views on the classroom as a complex adaptive system are presented and the phenomenon which is constantly at work and affecting each and every CAS - the entropy - is brought to discussion. A specific type of entropy for social groups, defined as social interactive entropy, is also introduced as an attempt to promote reflection on how this phenomenon affects the behavior of a classroom under a complex perspective and how it influences such a social CAS by providing or restricting conditions for interaction and, hence, learning to emerge.

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Khan, Shakir, V. Saravanan, Gnanaprakasam C. N, T. Jaya Lakshmi, Nabamita Deb, and Nashwan Adnan Othman. "Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms." Computational Intelligence and Neuroscience 2022 (March 24, 2022): 1–8. http://dx.doi.org/10.1155/2022/9985933.

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With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data. Based on the k-anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protection model to protect user privacy in a fine-grained manner. By distinguishing solid and weak sensitive attribute values, the constraints on sensitive attributes are improved, and the sensitive information is reduced for the leakage probability of vital information to achieve the safety of medical data sharing. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. Data analysis and experimental results show that this method can minimize execution time while improving data accuracy and service quality, which is more effective than existing solutions. The limits of solid and weak on sensitive qualities are enhanced, sensitive data are reduced, and the chance of crucial data leakage is lowered, all of which contribute to the security of healthcare data exchange. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. The scope of this research is that this paper enhances data accuracy while minimizing the algorithm’s execution time.

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Li, Zuozhi, and Jie Jiang. "Entropy model of dissipative structure on corporate social responsibility." IOP Conference Series: Earth and Environmental Science 69 (June 2017): 012126. http://dx.doi.org/10.1088/1755-1315/69/1/012126.

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Yuan, Peiyan, Huadong Ma, and Huiyuan Fu. "Hotspot-entropy based data forwarding in opportunistic social networks." Pervasive and Mobile Computing 16 (January 2015): 136–54. http://dx.doi.org/10.1016/j.pmcj.2014.06.003.

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Edsinger, Eric, Reuven Pnini, Natsumi Ono, Ryoko Yanagisawa, Kathryn Dever, and Jonathan Miller. "Social tolerance in Octopus laqueus—A maximum entropy model." PLOS ONE 15, no. 6 (June 10, 2020): e0233834. http://dx.doi.org/10.1371/journal.pone.0233834.

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Fonseca, Sofia, João Milho, Pedro Passos, Duarte Araújo, and Keith Davids. "Approximate Entropy Normalized Measures for Analyzing Social Neurobiological Systems." Journal of Motor Behavior 44, no. 3 (May 2012): 179–83. http://dx.doi.org/10.1080/00222895.2012.668233.

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Bailey, Kenneth D. "Talcott parsons, social entropy theory, and living systems theory." Behavioral Science 39, no. 1 (1994): 25–45. http://dx.doi.org/10.1002/bs.3830390103.

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Jizba, Petr, and Jan Korbel. "The Statistical Foundations of Entropy." Entropy 23, no. 10 (October 19, 2021): 1367. http://dx.doi.org/10.3390/e23101367.

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During the last few decades, the notion of entropy has become omnipresent in many scientific disciplines, ranging from traditional applications in statistical physics and chemistry, information theory, and statistical estimation to more recent applications in biology, astrophysics, geology, financial markets, or social networks [...]

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Mendoza Urdiales, Román Alejandro, Andrés García-Medina, and José Antonio Nuñez Mora. "Measuring information flux between social media and stock prices with Transfer Entropy." PLOS ONE 16, no. 9 (September 23, 2021): e0257686. http://dx.doi.org/10.1371/journal.pone.0257686.

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Transfer Entropy was applied to analyze the correlations and flow of information between 200,500 tweets and 23 of the largest capitalized companies during 6 years along the period 2013-2018. The set of tweets were obtained applying a text mining algorithm and classified according to daily date and company mentioned. We proposed the construction of a Sentiment Index applying a Natural Processing Language algorithm and structuring the sentiment polarity for each data set. Bootstrapped Simulations of Transfer Entropy were performed between stock prices and Sentiment Indexes. The results of the Transfer Entropy simulations show a clear information flux between general public opinion and companies’ stock prices. There is a considerable amount of information flowing from general opinion to stock prices, even between different Sentiment Indexes. Our results suggest a deep relationship between general public opinion and stock prices. This is important for trading strategies and the information release policies for each company.

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Davis, Philip. "Entropy and Society: Can the Physical/Mathematical Notions of Entropy Be Usefully Imported into the Social Sphere?" Journal of Humanistic Mathematics 1, no. 1 (January 2011): 119–36. http://dx.doi.org/10.5642/jhummath.201101.09.

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Domotor, Zoltan. "Probability kinematics, conditionals, and entropy principles." Synthese 63, no. 1 (April 1985): 75–114. http://dx.doi.org/10.1007/bf00485956.

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Fu, Weina, Shuai Liu, and Gautam Srivastava. "Optimization of Big Data Scheduling in Social Networks." Entropy 21, no. 9 (September 17, 2019): 902. http://dx.doi.org/10.3390/e21090902.

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In social network big data scheduling, it is easy for target data to conflict in the same data node. Of the different kinds of entropy measures, this paper focuses on the optimization of target entropy. Therefore, this paper presents an optimized method for the scheduling of big data in social networks and also takes into account each task’s amount of data communication during target data transmission to construct a big data scheduling model. Firstly, the task scheduling model is constructed to solve the problem of conflicting target data in the same data node. Next, the necessary conditions for the scheduling of tasks are analyzed. Then, the a periodic task distribution function is calculated. Finally, tasks are scheduled based on the minimum product of the corresponding resource level and the minimum execution time of each task is calculated. Experimental results show that our optimized scheduling model quickly optimizes the scheduling of social network data and solves the problem of strong data collision.

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Rödder, Wilhelm, Andreas Dellnitz, Friedhelm Kulmann, Sebastian Litzinger, and Elmar Reucher. "Bipartite Structures in Social Networks: Traditional versus Entropy-Driven Analyses." Entropy 21, no. 3 (March 13, 2019): 277. http://dx.doi.org/10.3390/e21030277.

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A special type of social networks is the so-called affiliation network, consisting of two modes of vertices: actors and events. Up to now, in the undirected case, the closeness of actors in such networks has been measured by their jointly-attended events. Indirect contacts and attenuated and directed links are of minor interest in affiliation networks. These flaws make a veritable estimation of, e.g., possible message transfers amongst actors questionable. In this contribution, first, we discuss these matters from a graph-theoretical point of view. Second, so as to avoid the identified weaknesses, we propose an up-and-coming entropy-based approach for modeling such networks in their generic structure, replacing directed (attenuated) links by conditionals: if-then. In this framework, the contribution of actors and events to a reliable message transfer from one actor to another—even via intermediaries—is then calculated applying the principle of maximum entropy. The usefulness of this new approach is demonstrated by the analysis of an affiliation network called “corporate directors”.

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Huang Fei-Hu, Peng Jian, and Ning Li-Miao. "Opinion evolution model of social network based on information entropy." Acta Physica Sinica 63, no. 16 (2014): 160501. http://dx.doi.org/10.7498/aps.63.160501.

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Yurtcicek Ozaydin, Seval, and Fatih Ozaydin. "Deep Link Entropy for Quantifying Edge Significance in Social Networks." Applied Sciences 11, no. 23 (November 25, 2021): 11182. http://dx.doi.org/10.3390/app112311182.

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Through online political communications, fragmented groups appear around ideological lines, which might form echo chambers if the communications within like-minded groups are dominant over the communications among different-minded groups, potentially contributing to political polarization and extremism. The antidote is the interactions between individuals who constitute social bridges between different minded groups. Hence, exploring the significance of connections between the individuals of a network is a center of attraction especially for the global connectivity and diffusion in networks. Based on the divergence of probability distributions of pairs of nodes, Link Entropy (LE) is a recently proposed method outperforming the others in quantifying edge significance. In this work, considering that the adjacent nodes of the two nodes of an edge are also in charge in determining its significance, we propose the Deep Link Entropy (DLE) method for a more precise quantification through taking into account the uncertainty distributions of the adjacent nodes as well. We show experimentally that DLE significantly outperforms LE especially in large-scale complex network with several groups or communities. We believe our method contributes to not only online political communications but a wide range of fields from biology to quantum networks, where edge significance has an operational meaning.

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47

Chakrabarty, Gurupada. "An Alternative Social Welfare Function Based on Theil's Entropy Measure." Artha Vijnana: Journal of The Gokhale Institute of Politics and Economics 37, no. 3 (September 1, 1995): 251. http://dx.doi.org/10.21648/arthavij/1995/v37/i3/115975.

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Zhao, Jichang, Xiao Liang, and Ke Xu. "Competition between Homophily and Information Entropy Maximization in Social Networks." PLOS ONE 10, no. 9 (September 3, 2015): e0136896. http://dx.doi.org/10.1371/journal.pone.0136896.

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Yang, Wenyin, Guojun Wang, Md Zakirul Alam Bhuiyan, and Kim-Kwang Raymond Choo. "Hypergraph partitioning for social networks based on information entropy modularity." Journal of Network and Computer Applications 86 (May 2017): 59–71. http://dx.doi.org/10.1016/j.jnca.2016.10.002.

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Best, Steven. "Chaos and entropy: Metaphors in postmodern science and social theory." Science as Culture 2, no. 2 (January 1991): 188–226. http://dx.doi.org/10.1080/09505439109526302.

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Journal articles on the topic 'Social entropy'

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