The quantitative analysis of digitized historical documents has begun in earnest in recent years. Text classification is of particular importance for quantitative historical analysis because it helps to search literature efficiently and to determine the important subjects of a particular age. While numerous historians have joined together to classify large-scale historical documents, consistent classification among individual researchers has not been achieved. In this study, we present a classification method for large-scale historical data that uses a recently developed supervised learning algorithm called the Hierarchical Attention Network (HAN). By applying various classification methods to the Annals of the Joseon Dynasty (AJD), we show that HAN is more accurate than conventional techniques with word-frequency-based features. HAN provides the extent that a particular sentence or word contributes to the classification process through a quantitative value called ’attention’. We extract the representative keywords from various categories by using the attention mechanism and show the evolution of the keywords over the 472-year span of the AJD. Our results reveal that largely two groups of event categories are found in the AJD. In one group, the representative keywords of the categories were stable over long periods while the keywords in the other group varied rapidly, exhibiting repeatedly changing characteristics of the categories. Observing such macroscopic changes of representative words may provide insight into how a particular topic changes over a historical period.

The spread of behavior in a society has two major features: the synergy of multiple spreaders and the dominance of hubs. While strong synergy is known to induce mixed-order transitions (MOTs) at percolation, the effects of hubs on the phenomena are yet to be clarified. By analytically solving the generalized epidemic process on random scale-free networks with the power-law degree distribution ${p}_{k}\ensuremath{\sim}{k}^{\ensuremath{-}\ensuremath{\alpha}}$, we clarify how the dominance of hubs in social networks affects the conditions for MOTs. Our results show that, for $\ensuremath{\alpha}<4$, an abundance of hubs drive MOTs, even if a synergistic spreading event requires an arbitrarily large number of adjacent spreaders. In particular, for $2<\ensuremath{\alpha}<3$, we find that a global cascade is possible even when only synergistic spreading events are allowed. These transition properties are substantially different from those of cooperative contagions, which are another class of synergistic cascading processes exhibiting MOTs.

Almost by definition, radical innovations create a need to revise existing classification systems. In this paper, we argue that classification system changes and patent reclassification are common and reveal interesting information about technological evolution. To support our argument, we present three sets of findings regarding classification volatility in the U.S. patent classification system. First, we study the evolution of the number of distinct classes. Reconstructed time series based on the current classification scheme are very different from historical data. This suggests that using the current classification to analyze the past produces a distorted view of the evolution of the system. Second, we study the relative sizes of classes. The size distribution is exponential so classes are of quite different sizes, but the largest classes are not necessarily the oldest. To explain this pattern with a simple stochastic growth model, we introduce the assumption that classes have a regular chance to be split. Third, we study reclassification. The share of patents that are in a different class now than they were at birth can be quite high. Reclassification mostly occurs across classes belonging to the same 1-digit NBER category, but not always. We also document that reclassified patents tend to be more cited than non-reclassified ones, even after controlling for grant year and class of origin.

Painting is an art form that has long functioned as a major channel for the creative expression and communication of humans, its evolution taking place under an interplay with the science, technology, and social environments of the times. Therefore, understanding the process based on comprehensive data could shed light on how humans acted and manifested creatively under changing conditions. Yet, there exist few systematic frameworks that characterize the process for painting, which would require robust statistical methods for defining painting characteristics and identifying human’s creative developments, and data of high quality and sufficient quantity. Here we propose that the color contrast of a painting image signifying the heterogeneity in inter-pixel chromatic distance can be a useful representation of its style, integrating both the color and geometry. From the color contrasts of paintings from a large-scale, comprehensive archive of 179 853 high-quality images spanning several centuries we characterize the temporal evolutionary patterns of paintings, and present a deep study of an extraordinary expansion in creative diversity and individuality that came to define the modern era.

N-gram Web Service and Stylometric Analysis of Korean Historical Documents