Ultraslow diffusion in language: Dynamics of appearance of already popular adjectives on Japanese blogs
This provides insights into fundamental language dynamics for researchers in computational linguistics and social media analysis, though it is incremental as it applies existing models to new data.
The paper tackled the problem of understanding the dynamics of word appearance in social media data by analyzing approximately three billion Japanese blog articles over six years, finding that a random diffusion model based on a power-law forgetting process explains word appearance and reproduces ultraslow diffusion and other statistical properties.
What dynamics govern a time series representing the appearance of words in social media data? In this paper, we investigate an elementary dynamics, from which word-dependent special effects are segregated, such as breaking news, increasing (or decreasing) concerns, or seasonality. To elucidate this problem, we investigated approximately three billion Japanese blog articles over a period of six years, and analysed some corresponding solvable mathematical models. From the analysis, we found that a word appearance can be explained by the random diffusion model based on the power-law forgetting process, which is a type of long memory point process related to ARFIMA(0,0.5,0). In particular, we confirmed that ultraslow diffusion (where the mean squared displacement grows logarithmically), which the model predicts in an approximate manner, reproduces the actual data. In addition, we also show that the model can reproduce other statistical properties of a time series: (i) the fluctuation scaling, (ii) spectrum density, and (iii) shapes of the probability density functions.