The Application of Network Based Embedding in Local Topic Detection from Social Media

Abstract

Detecting local topic from social media is an important task for many applications, such as local event discovery and activity recommendation. Recent years have witnessed growing interest in utilizing spatio-temporal social media for local topic detection. However, conventional topic models consider keywords as independent items, which suffer great limitations in modeling short texts from social media. Therefore, some studies introduce embedding into topic models to preserve the semantic correlation among keywords of short texts. Nevertheless, due to the lack of rich contexts in social media, the performance of these embedding based topic models still remain unsatisfactory. In order to enrich the contexts of keywords, we propose two network based embedding methods, both of which can generate rich contexts for keywords by random walks and produce coherent keyword embeddings for topic modeling. Besides, processing continuous spatio-temporal information in social media is also very challenging. Most of the existing methods simply split time and location into equal-size units, which fall short in capturing the continuity of spatio-temporal information. To address this issue, we present a hotspot detection algorithm to identify spatial and temporal hotspots, which can address spatio-temporal continuity and alleviate data sparsity. Finally, the experiments show that the performance of our methods has been improved significantly compared to the state-of-the-art methods.