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Abstract
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.
Highlights
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
Indicators of the power laws of such time series belong to the spectrum of indicators of complexity (1, 3), (0.5, 1.5), (0, 1) and, Twitter, which generates such time series of microposts, is in the self-organized criticality (SOC) state or the SupC state. e social network is capable of generating extreme events, which are avalanches of microposts of all sizes corresponding to the following tweet ids: “2016 United States Presidential Election,” “Women’s March,” “Hurricanes Harvey,” “Hurricanes Irma,” “Immigration and Travel Ban,” “Charlottesville,” “2018 US Congressional Election,” and “Ireland 8th.”
For all time series of the first class s 1 and βs 1, which corresponds to the presence of pink noise and, being Twitter in the SOC or the SupC states. e existence of a dependency (4) for the time series of microposts means that the current numbers of microposts largely depend on the past number of microposts generated by Twitter, as well as the absence of characteristic times at which information about previous occurrences of microposts would be lost
Summary
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. Power laws describe large-scale invariance in the structure of time series of microposts generated by the self-organized critical social network.
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