Discovering novel emergency events in text streams

Dmitriy Deviatkin, Artem Shelmanov, Daniil Larionov

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

We present text processing framework for discovering emergency related events via analysis of information sources such as social networks. The framework performs focused crawling of messages, text parsing, information extraction, detection of messages related to emergencies, as well as automatic novel event discovering and matching them across different information sources. For detection of emergency-related messages, we use CNN and word embeddings. For discovering novel events and matching them across different sources, we propose a multimodal topic model enriched with spatial information and a method based on Jensen–Shannon divergence. The components of the framework are experimentally evaluated on Twitter and Facebook data.

Original languageEnglish
Pages (from-to)208-215
Number of pages8
JournalCEUR Workshop Proceedings
Volume2277
Publication statusPublished - 2018
Externally publishedYes
EventSelected Papers of the 20th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2018 - Moscow, Russian Federation
Duration: 9 Oct 201812 Oct 2018

Keywords

  • Event detection
  • Monitoring
  • Named entity recognition
  • Novel topic
  • Text processing
  • Topic modelling

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