scholarly journals Identification of Self-Organized Critical State on Twitter Based on the Retweets’ Time Series Analysis

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Andrey Dmitriev ◽  
Victor Dmitriev
Keyword(s):  
Time Series ◽  
Presidential Election ◽  
Long Memory ◽  
Critical State ◽  
Critical Conditions ◽  
Power Law Distribution ◽  
Self Organized ◽  
History Of ◽  
Supercritical Phase ◽  
Critical Phases

There is a number of studies, in which it is established that the observed flows of microposts generated by microblogging social networks (e.g., Twitter) are characterized by avalanche-like behavior. Time series of microposts depicting such streams are the time series with a power-law distribution, with 1/f noise and long memory. Despite this, there are no studies devoted to the detection and analysis of self-organized critical state, subcritical phase, and supercritical phase. The presented paper is devoted to the detection and investigation of such critical states and phases. An algorithm is proposed that allowed to detect of critical phases and critical conditions on Twitter, based on the analysis of retweets time series corresponding to the three debates of the 2016 United States Presidential Election, as the most popular debate in the history of America, collecting 84 million live views.

scholarly journals Self-Organized Criticality on Twitter: Phenomenological Theory and Empirical Investigation Based on Data Analysis Results

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Andrey Dmitriev ◽  
Victor Dmitriev ◽  
Stepan Balybin
Keyword(s):  
Time Series ◽  
Critical State ◽  
Empirical Investigation ◽  
Phenomenological Theory ◽  
Self Organization ◽  
Protest Movements ◽  
Self Organized ◽  
Self Organized Criticality ◽  
Stochastic Sources ◽  
Adiabatic Mode

Recently, there has been an increasing number of empirical evidence supporting the hypothesis that spread of avalanches of microposts on social networks, such as Twitter, is associated with some sociopolitical events. Typical examples of such events are political elections and protest movements. Inspired by this phenomenon, we built a phenomenological model that describes Twitter’s self-organization in a critical state. An external manifestation of this condition is the spread of avalanches of microposts on the network. The model is based on a fractional three-parameter self-organization scheme with stochastic sources. It is shown that the adiabatic mode of self-organization in a critical state is determined by the intensive coordinated action of a relatively small number of network users. To identify the critical states of the network and to verify the model, we have proposed a spectrum of three scaling indicators of the observed time series of microposts.

scholarly journals A Review of Mormon Studies in China

Religions ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 375
Author(s):  
Hongmeng Cheng
Keyword(s):  
Presidential Election ◽  
National Level ◽  
Chinese Government ◽  
Journal Articles ◽  
Internal Factors ◽  
History Of ◽  
Us Presidential Election ◽  
Chinese Scholars ◽  
Mitt Romney ◽  
High Level

Mormon studies in China began in the early 1990s and can be divided into three phases between the years of 2004 and 2017. The first Master’s and Doctoral theses on Mormonism were both published in 2004, and journal articles have also been increasing in frequency since then. The year of 2012 saw a peak, partly because Mormon Mitt Romney won the Republican nomination for the 2012 US presidential election. In 2017, a national-level project, Mormonism and its Bearings on Current Sino-US Relations, funded by the Chinese government, was launched. However, Mormon studies in China is thus far still in its infancy, with few institutions and a small number of scholars. Academic works are limited in number, and high-level achievements are very few. Among the published works, the study of the external factors of Mormonism is far more prevalent than research on its internal factors. Historical, sociological, and political approaches far exceed those of philosophy, theology, and history of thoughts. To Mormon studies, Chinese scholars can and should be making unique contributions, but the potential remains to be tapped.

scholarly journals On nonparametric regression for bivariate circular long-memory time series

2021 ◽  
Author(s):  
Jan Beran ◽  
Britta Steffens ◽  
Sucharita Ghosh
Keyword(s):  
Time Series ◽  
Nonparametric Regression ◽  
Long Range ◽  
Long Memory ◽  
Regression Function ◽  
Confidence Bands ◽  
Long Range Dependence ◽  
Asymptotic Results ◽  
Memory Time ◽  
Circular Time Series

AbstractWe consider nonparametric regression for bivariate circular time series with long-range dependence. Asymptotic results for circular Nadaraya–Watson estimators are derived. Due to long-range dependence, a range of asymptotically optimal bandwidths can be found where the asymptotic rate of convergence does not depend on the bandwidth. The result can be used for obtaining simple confidence bands for the regression function. The method is illustrated by an application to wind direction data.

Fluctuations at the self-organized critical state

1997 ◽  
Vol 56 (6) ◽  
pp. 6710-6718 ◽  
Author(s):  
Hans-Henrik Stølum
Keyword(s):  
Critical State ◽  
The Self ◽  
Self Organized

scholarly journals The CSS and The Two-Staged Methods for Parameter Estimation in SARFIMA Models

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Erol Egrioglu ◽  
Cagdas Hakan Aladag ◽  
Cem Kadilar
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Simulation Study ◽  
Long Memory ◽  
Moving Average ◽  
Mean Square ◽  
Sample Sizes ◽  
Advantages And Disadvantages

Seasonal Autoregressive Fractionally Integrated Moving Average (SARFIMA) models are used in the analysis of seasonal long memory-dependent time series. Two methods, which are conditional sum of squares (CSS) and two-staged methods introduced by Hosking (1984), are proposed to estimate the parameters of SARFIMA models. However, no simulation study has been conducted in the literature. Therefore, it is not known how these methods behave under different parameter settings and sample sizes in SARFIMA models. The aim of this study is to show the behavior of these methods by a simulation study. According to results of the simulation, advantages and disadvantages of both methods under different parameter settings and sample sizes are discussed by comparing the root mean square error (RMSE) obtained by the CSS and two-staged methods. As a result of the comparison, it is seen that CSS method produces better results than those obtained from the two-staged method.

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