Large-scale forecasting of information spreading

Abstract

This research proposes a system based on a combination of various components for parallel modelling and forecasting the processes in networks with data assimilation from the real network. The main novelty of this work consists of the assimilation of data for forecasting the processes in social networks which allows improving the quality of the forecast. The social network VK was considered as a source of information for determining types of entities and the parameters of the model. The main component is the model based on a combination of internal sub-models for more realistic reproduction of processes on micro (for single information message) and meso (for series of messages) levels. Moreover, the results of the forecast must not lose their relevance during the calculations. In order to get the result of the forecast for networks with millions of nodes in reasonable time, the process of simulation has been parallelized. The accuracy of the forecast is estimated by MAPE, MAE metrics for micro-scale, the Kolmogorov-Smirnov criterion for aggregated dynamics. The quality in the operational regime is also estimated by the number of batches with assimilated data to achieve the required accuracy and the ratio of calculation time in the frames of the forecasting period. In addition, the results include experimental studies of functional characteristics, scalability, as well as the performance of the system.

Large Language Models, Social Demography, and Hegemony: Comparing Authorship in Human and Synthetic Text

“…To compare the distributions, we use the Kolmogorov-Smirnov (KS) statistical test. The KS test, along with other analyses which compare distributions like Kullback-Liebler divergence, is widely used in data science and machine learning as a way to compare the likelihood that two continuous variables were drawn from the same distribution (in the two sample test case) Severiukhina et al (2020). Formally, we are comparing the empirical distribution functions of human authors F and AI authors G for each LIWC feature i for each sample size n for humans and m for AI.…”