Scientific knowledge percolation process and social impact: A case study on the biotechnology and microbiology perceptions on Twitter

Barros, Beatríz; Fernández-Zubieta, Ana; Fidalgo-Merino, Raul; Triguero, Francisco Herrera

doi:10.1093/scipol/scy022

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…Studies of the self-organization of the social networks into the critical state as a result of percolation phase transitions are presented in [10][11][12][13][14][15]. For example, the reaction of Twitter users to the scientific knowledge percolation process was studied by B. Barros and coauthors [16].…”

Section: Introductionmentioning

confidence: 99%

Identification of Self‐Organized Critical State on Twitter Based on the Retweets’ Time Series Analysis

Dmitriev

2021

Complexity

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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.

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Section: Introductionmentioning

confidence: 99%

Identification of Self‐Organized Critical State on Twitter Based on the Retweets’ Time Series Analysis

Dmitriev

2021

Complexity

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“…In other words, the knowledge diffusion process, at least insofar as measured by citations, is proceeding with a step change. This phenomenon is strongly reminiscent of knowledge diffusion as a percolation process (Bogaert et al 2000;Barros et al 2018;Tang et al 2020) within the complex network of researchers in fields related to the SB; t = s is the critical point on the time scale (in citation years after publication).…”

Section: Time-developments Of the Citation Impact Of Sbs For S = 5 To 10mentioning

confidence: 99%

Sleeping beauties gain impact in overdrive mode

Raan

2021

Scientometrics

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In this study we focus on characteristics of SBs that have not or hardly been investigated previously. We find that the choice of the awakening period in the selection of SBs has consequences for the measured citation patterns. Focusing on medical SBs we analyze patterns in the time-development of the citation impact of SBs; the influence of self-citations on the awakening process; and the occurrence of medical research fields to which the SBs and their citing papers belong. An important finding is that SBs are generally characterized by a sleep that becomes less and less deep instead of a permanent deep sleep. The sleeping period is followed by a phase-transition-like jump as a start of the awakening period and a remarkable regularity is found for the citation impact immediately before and after the jump.

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“…Recently, numerous evidences of self-organization of real-world systems of various origins to the edge of a phase transition have been obtained (e.g., see papers [5,6]). Tus, SOC is a characteristic of social interaction networks (e.g., see papers [7][8][9]) and online social networks (e.g., see papers [10][11][12][13][14][15]) such as Twitter. SOB is a characteristic of the brain (e.g., see the paper [16]).…”

Section: Introductionmentioning

confidence: 99%

Twitter Self-Organization to the Edge of a Phase Transition: Discrete-Time Model and Effective Early Warning Signals in Phase Space

Dmitriev,

Lebedev,

Kornilov

et al. 2023

Complexity

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Many real-world systems of various origins are capable of self-organization to the edge of a phase transition, characterized by avalanche-like behavior. Therefore, it is important, by observing the behavior of early warning measures for dynamical series generated by systems, to timely see the early warning signals (precursors) of such self-organization and, if necessary, take preventive measures. To date, convincing evidence of self-organization to the edge of a phase transition has been obtained, but no effective precursors for this self-organization have been found. This research explores precursors for the Twitter self-organization based on the analysis of the behavior of measures directly related to the critical slowdown of the network and measures of the phase space reconstructed by the Takens method for the series of the number of network users creating avalanches of retweets in the network, corresponding to the three debates of the 2016 United States Presidential Election. We hydrated the relevant Tweet IDs, which were obtained from the Harvard Dataverse using the Social Feed Manager, to form this series. Preliminarily, we explore the potential of measures for early detection of self-organization of sandpile cellular automata as systems with Twitter-equivalent self-organization mechanisms. The equivalence is justified in the proposed discrete-time model for Twitter self-organization to the edge of a phase transition. It is found that there are more moments of the Twitter self-organization than the moments of time when debates started, and Twitter stays at the edge of a phase transition longer than the debate lasts. The effective measures, as the measures with the lowest number of false early warning signals, among all studied measures and for all studied systems, are dispersion and correlation dimension. Obtained results are practically important in the design and implementation of early warning systems for the systems with similar mechanisms for sandpile cellular automata self-organization to the edge of a phase transition.

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Scientific knowledge percolation process and social impact: A case study on the biotechnology and microbiology perceptions on Twitter

Cited by 5 publications

References 33 publications

Identification of Self‐Organized Critical State on Twitter Based on the Retweets’ Time Series Analysis

Identification of Self‐Organized Critical State on Twitter Based on the Retweets’ Time Series Analysis

Sleeping beauties gain impact in overdrive mode

Twitter Self-Organization to the Edge of a Phase Transition: Discrete-Time Model and Effective Early Warning Signals in Phase Space

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