WhatsUp: An event resolution approach for co-occurring events in social media

Abstract

The rapid growth of social media networks has resulted in the generation of a vast data amount, making it impractical to conduct manual analyses to extract newsworthy events. Thus, automated event detection mechanisms are invaluable to the community. However, a clear majority of the available approaches rely only on data statistics without considering linguistics. A few approaches involved linguistics, only to extract textual event details without the corresponding temporal details. Since linguistics define words’ structure and meaning, a severe information loss can happen without considering them. Targeting this limitation, we propose a novel method named WhatsUp to detect temporal and fine-grained textual event details, using linguistics captured by self-learned word embeddings and their hierarchical relationships and statistics captured by frequency-based measures. We evaluate our approach on recent social media data from two diverse domains and compare the performance with several state-of-the-art methods. Evaluations cover temporal and textual event aspects, and results show that WhatsUp notably outperforms state-of-the-art methods. We also analyse the efficiency, revealing that WhatsUp is sufficiently fast for (near) real-time detection. Further, the usage of unsupervised learning techniques, including self-learned embedding, makes our approach expandable to any language, platform and domain and provides capabilities to understand data-specific linguistics.