Local Topic Detection Using Word Embedding from Spatio-Temporal Social Media

Abstract

Local topic detection from spatio-temporal social media (e.g. Twitter) plays an important role in many applications, therefore, it has attracted a surge of research attention in recent years. However, most existing studies consider time and location separately, they often assume tweets are correlated as long as their time is adjacent or their location is neighboring. But in reality, only tweets posted at both adjacent time and neighboring location tend to talk about the same local topic. To address this issue, we propose a network based embedding model to capture the correlation between time and location, and jointly model spatio-temporal information and semantic information together. This embedding model can ensure that the generated keyword embeddings are semantically coherent and spatio-temporally close. Based on the keyword embeddings, we present a novel topic model to obtain high-quality local topics. This topic model presumes that each tweet is represented by only one topic and takes the background mode of words into consideration to address the concise and noisy problem of Twitter. The experiments demonstrate that the effectiveness and efficiency of our method have been improved significantly compared to the state-of-the-art existing methods.