Abstract
Online social networks have become an essential communication channel for the broad and rapid sharing of information. Currently, the mechanics of such information-sharing is captured by the notion of cascades, which are tree-like networks comprised of (re)sharing actions. However, it is still unclear what factors drive cascade growth. Moreover, there is a lack of studies outside Western countries and platforms such as Facebook and Twitter. In this work, we aim to investigate what factors contribute to the scope of information cascading and how to predict this variation accurately. We examine six machine learning algorithms for their predictive and interpretative capabilities concerning cascades’ structural metrics (width, mass, and depth). To do so, we use data from a leading Russian-language online social network VKontakte capturing cascades of 4,424 messages posted by 14 news outlets during a year. The results show that the best models in terms of predictive power are Gradient Boosting algorithm for width and depth, and Lasso Regression algorithm for the mass of a cascade, while depth is the least predictable. We find that the most potent factor associated with cascade size is the number of reposts on its origin level. We examine its role along with other factors such as content features and characteristics of sources and their audiences.
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This work is an output of a research project implemented as part of the Basic Research Program at the National Research University Higher School of Economics (HSE University).
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A Appendix
A Appendix
A graph showing top-10 of the most tangible predicting features used as input by the Lasso Regression algorithm for cascade’s mass prediction.
A plot displaying negative to positive values proportion of Lasso Regression variables’ coefficients.
Plots showing observation-level effects of the number of posts first-level reposts on cascade’s depth (on the left) and width (on the right). The multiple black lines are individual conditional expectation (ICE) curves, while the red line stands for the averaged values across all predictions [24] by Gradient Boosting algorithm.
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Moroz, A., Pashakhin, S., Koltsov, S. (2021). Modeling Cascade Growth: Predicting Content Diffusion on VKontakte. In: Antonyuk, A., Basov, N. (eds) Networks in the Global World V. NetGloW 2020. Lecture Notes in Networks and Systems, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-030-64877-0_12
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