Reconstruction of Unfolding Sub-Events From Social Media Posts

Abstract

Event detection plays a crucial role in social media analysis, which usually concludes sub-event detection and correlation. In this article, we present a method for reconstructing the unfolding sub-event relations in terms of external expert knowledge. First, a Single Pass Clustering method is utilized to summarize massive social media posts. Second, a Label Propagation Algorithm is introduced to detect the sub-event according to the expert labeling. Third, a Word Mover’s Distance method is used to measure the correlation between the relevant sub-events. Finally, the Markov Chain Monte Carlo simulation method is presented to regenerate the popularity of social media posts. The experimental results show that the popularity dynamic of the empirical social media sub-events is consistent with the data generated by the proposed method. The evaluation of the unfolding model is 50.52% ∼ 88% higher than that of the random null model in the case of “Shanghai Tesla self-ignition incident.” This work is helpful for understanding the popularity mechanism of the unfolding events for online social media.