Online event detection and tracking in social media based on neural similarity metric learning

Abstract

The ever-growing number of users makes social media a valuable information source about recent events. Event detection and tracking plays an important role in decision-making and public management. Despite recent progress, the performance of event detection and tracking is still limited. The majority of existing work lacks an effective way to judge whether a text related to a certain event, due to the limitations of semantic representation and heuristic similarity metric. In this paper, we present an online event detection and tracking method based on similarity metric learning using neural network. Our method first trains a classification model to identify event related texts. To detect and track events, we adopt a clustering-based approach. Specifically, we use neural network to jointly learn a similarity metric and low dimension representation of events, and then use a memory module to store and update event representation. Experiments on Twitter dataset show the effectiveness of our proposed method.