Akademik Veri Yönetim Sistemi
17th IEEE Annual International Conference on Intelligence and Security Informatics (ISI), Shenzhen, Çin, 1 - 03 Temmuz 2019, ss.68-73
As terrorists are losing against counter-terrorism efforts, they turn to manipulating cryptocurrency prices through online social communities to gain illicit profit to fund their operations. Modeling temporal online social networks (OSNs) of these communities can possibly help to provide useful intelligence about these malicious activities. However, existing techniques do not learn sufficiently from diverse features to enable prediction and simulation of online social behavior. Research on simulating temporal OSN behavior is not widely available. This research developed and validated a deep learning approach, named Temporal Network Model (TNM), to modeling the complex features and dynamic behavior exhibited in the temporal OSNs of online communities. Using extensive features extracted from line-grained data, TNM consists of weighted time series models, user and link prediction models, and temporal dependency model that predict respectively the macroscopic behavior, microscopic user participation and events, and time stamps of the events. Evaluation was done in comparison with a benchmark approach to examine TNM's perlbrmance on predicting and simulating behavior of 42,627 users in 440,906 events on the Reddit cryptocurrency community during July-August of 2017. Results show that TNM outperformed the benchmark in 5 out of 8 simulation metrics. TNM achieved consistently better perlOrmance in user activity prediction, and performed generally better in structural (network-level) prediction. The research provides new findings on simulating temporal OSNs and new predictive analytics for understanding online social behavior.