Detecting Popular Social Events through Limited Observation with Deep Survival Analysis
For social network analysts and digital marketers, this work offers a proactive approach to identify popular trends early, but the results are incremental as it applies existing deep survival analysis to a known problem.
This research proposes a deep survival analysis-based method to predict whether an information cascade will become highly popular by observing only early-stage data. The method was tested on real-world datasets from Twitter, Weibo, and Digg, achieving improved prediction accuracy over baselines.
Users increasing activity across various social networks made it the most widely used platform for exchanging and propagating information among individuals. To spread information within a network, a user initially shared information on a social network, and then other users in direct contact with him might have shared that information. Information expanded throughout the network by repeatedly following this process. A set of information that became popular and was repeatedly shared by different individuals was called popular trends. Identifying and analyzing these trends led to valuable insights into the dynamics of information dissemination within a network. However, more importantly, proactive approaches emerged. In other words, by observing the dissemination pattern of a piece of information in the early stages of expansion, it became possible to determine whether this cascade would become highly popular in the future. This research aimed to predict and detect popular trends in social networks by observing limited early-stage data and using a deep survival analysis-based method. This model could play a significant role in improving recommendation systems, predicting the reach of digital content, and assisting in optimal decision-making in digital marketing. Ultimately, the proposed method was tested on various real-world anonymized datasets from Twitter, Weibo, and Digg.