Event
Prediction in Online Social Networks (pp064-094)
Leonard Tan, Thuan Pham, Kei Ho Hang and Seng Kok Tan
doi:
https://doi.org/10.26421/JDI2.1-4
Abstracts:
Event prediction is a very important task in numerous
applications of interest like
fintech, medical, security, etc.
However, event prediction is a highly complex task because it is
challenging to classify, contains temporally changing themes of
discussion and heavy topic drifts. In this research, we present a
novel approach which leverages on the RFT
framework developed in \cite{tan2020discovering}.
This study addresses the challenge of accurately representing
relational features in observed complex social communication
behavior for the event prediction task; which recent graph learning
methodologies are struggling with. The concept here, is to firstly
learn the turbulent patterns of relational state transitions between
actors preceeding an event and then secondly, to evolve these
profiles temporally, in the event prediction process. The event
prediction model which leverages on the RFT framework discovers,
identifies and adaptively ranks relational turbulence as likelihood
predictions of event occurrences. Extensive experiments on
large-scale social datasets across important indicator tests for
validation, show that the RFT
framework performs comparably better by more than 10\%
to HPM
\cite{amodeo2011hybrid}
and other state-of-the-art baselines in event prediction.
Key words:
Event Prediction,
Artificial Intelligence, Topic Modeling, Wavelet Transformation,
Fractal Neural Networks