arXiv subjects

Jinhyuk Yun

Publications and source records attributed to Jinhyuk Yun.

22 records · Page 2Linked to original sources

arXiv2018

Build up of a subject classification system from collective intelligence

Systematized subject classification is essential for funding and assessing scientific projects. Conventionally, classification schemes are founded on the empirical knowledge of the group of experts; thus, the experts' perspectives have influenced the current systems of scientific classification. Those systems archived the current state-of-art in practice, yet the global effect of the accelerating scientific change over time has made the updating of the classifications system on a timely basis vertually impossible. To overcome the aforementioned limitations, we propose an unbiased classification scheme that takes advantage of collective knowledge; Wikipedia, an Internet encyclopedia edited by millions of users, sets a prompt classification in a collective fashion. We construct a Wikipedia network for scientific disciplines and extract the backbone of the network. This structure displays a landscape of science and technology that is based on a collective intelligence and that is more unbiased and adaptable than conventional classifications.

physics.soc-ph↗

arXiv2018

Early onset of structural inequality in the formation of collaborative knowledge, Wikipedia

We perform an in-depth analysis on the inequality in 863 Wikimedia projects. We take the complete editing history of 267,304,095 Wikimedia items until 2016, which not only covers every language edition of Wikipedia, but also embraces the complete versions of Wiktionary, Wikisource, Wikivoyage, etc. Our findings of common growth pattern described by the interrelations between four characteristic growth yardsticks suggest a universal law of communal data formation. In this encyclopaedic data set, we observe the interplay between the number of edits and the degree of inequality. In particular, the rapid increase in the Gini coefficient suggests that this entrenched inequality stems from the nature of such open-editing communal data sets, namely the abiogenesis of the supereditors' oligopoly. We show that these supereditor groups were created at the early stages of these open-editing media and are still active. Furthermore, our model considers both short-term and long-term memories to successfully elucidate the underlying mechanism of the establishment of oligarchy in Wikipedia. Our results anticipate a noticeable prospect of such communal databases in the future: the disparity will not be resolved spontaneously.

physics.soc-ph↗

arXiv2016

Intellectual interchanges in the history of the massive online open-editing encyclopedia, Wikipedia

Wikipedia is a free Internet encyclopedia with an enormous amount of content. This encyclopedia is written by volunteers with various backgrounds in a collective fashion; anyone can access and edit most of the articles. This open-editing nature may give us prejudice that Wikipedia is an unstable and unreliable source; yet many studies suggest that Wikipedia is even more accurate and self-consistent than traditional encyclopedias. Scholars have attempted to understand such extraordinary credibility, but usually used the number of edits as the unit of time, without consideration of real time. In this work, we probe the formation of such collective intelligence through a systematic analysis using the entire history of 34,534,110 English Wikipedia articles, between 2001 and 2014. From this massive data set, we observe the universality of both timewise and lengthwise editing scales, which suggests that it is essential to consider the real-time dynamics. By considering real time, we find the existence of distinct growth patterns that are unobserved by utilizing the number of edits as the unit of time. To account for these results, we present a mechanistic model that adopts the article editing dynamics based on both editor-editor and editor-article interactions. The model successfully generates the key properties of real Wikipedia articles such as distinct types of articles for the editing patterns characterized by the interrelationship between the numbers of edits and editors, and the article size. In addition, the model indicates that infrequently referred articles tend to grow faster than frequently referred ones, and articles attracting a high motivation to edit counterintuitively reduce the number of participants. We suggest that this decay of participants eventually brings inequality among the editors, which will become more severe with time.

physics.soc-ph↗

arXiv2015

Anatomy of Scientific Evolution

The quest for historically impactful science and technology provides invaluable insight into the innovation dynamics of human society, yet many studies are limited to qualitative and small-scale approaches. Here, we investigate scientific evolution through systematic analysis of a massive corpus of digitized English texts between 1800 and 2008. Our analysis reveals great predictability for long-prevailing scientific concepts based on the levels of their prior usage. Interestingly, once a threshold of early adoption rates is passed even slightly, scientific concepts can exhibit sudden leaps in their eventual lifetimes. We developed a mechanistic model to account for such results, indicating that slowly-but-commonly adopted science and technology surprisingly tend to have higher innate strength than fast-and-commonly adopted ones. The model prediction for disciplines other than science was also well verified. Our approach sheds light on unbiased and quantitative analysis of scientific evolution in society, and may provide a useful basis for policy-making.

physics.soc-ph↗