Detection of event onset using Twitter
Social Media generates information about news and events in real time. Given the vast amount of data available and the rate of information propagation, reliably identifying events is a challenge. Most state of the art techniques are post hoc techniques that detect an event after it happened. Our goal is to detect onset of an event as it is happening using the user generated information from Twitter streams. To achieve this goal, we use a discriminative model to identify change in the pattern of conversations over time. We use a topic evolution model to find credible events and eliminate random noise that is prevalent in many of the event detection models. The simplicity of the proposed model allows detect events quickly and efficiently, permitting discovery of events within minutes from the start of conversation about those conversations on Twitter. Our model is evaluated on a large scale Twitter corpus to detect events in real time. The proposed model is tested on other datasets to detect change over longer periods of time. The results indicate we can detect real events, within 3 to 8 minutes of it first appearing, with a lower degree of noise compared to other methods.
2016 International Joint Conference on Neural Networks (IJCNN)