Early Detection of User Dynamics Overheating Through Frequency Analysis of Time-Series Data

Masaki Aida, Kazuki Nakajima, Chisa Takano

Abstract: The excessive activation of user dynamics, such as online flaming, causes various social issues, making effective intervention based on early detection desirable. Current early detection methods identify increased user activity based on quantitative changes in time-series data, such as whether the number of social media posts exceeds a threshold. However, from a theoretical standpoint rooted in fundamental principles, it is expected that the precursor to excessive activation of user dynamics due to structural changes in social networks will manifest itself as the emergence of a low-frequency mode in the time series of user dynamics intensity. This research describes a method for the early detection of excessive activation of user dynamics by identifying the emergence of low-frequency modes through frequency spectrum analysis of actual SNS data, a method faster than the quantitative observation of time-series data.

External IDs:dblp:conf/dasc/AidaNT24