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Event detection and evolution in multi-lingual social streams

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Abstract

Real-life events are emerging and evolving in social and news streams. Recent methods have succeeded in capturing designed features of monolingual events, but lack of interpretability and multi-lingual considerations. To this end, we propose a multi-lingual event mining model, namely MLEM, to automatically detect events and generate evolution graph in multilingual hybrid-length text streams including English, Chinese, French, German, Russian and Japanese. Specially, we merge the same entities and similar phrases and present multiple similarity measures by incremental word2vec model. We propose an 8-tuple to describe event for correlation analysis and evolution graph generation. We evaluate the MLEM model using a massive human-generated dataset containing real world events. Experimental results show that our new model MLEM outperforms the baseline method both in efficiency and effectiveness.

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Acknowledgements

This work was supported by NSFC program (Grant Nos. 61872022, 61421003, U1636123), SKLSDE-2018ZX-16 and partly by the Beijing Advanced Innovation Center for Big Data and Brain Computing.

Author information

Authors and Affiliations

  1. Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100083, China

    Yaopeng Liu, Hao Peng & Jianxin Li

  2. State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100083, China

    Yaopeng Liu, Hao Peng & Jianxin Li

  3. Department of Computer Science and Engineering, HKUST, Hong Kong, 99907, China

    Yangqiu Song

  4. National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, 100029, China

    Xiong Li

Authors
  1. Yaopeng Liu
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  2. Hao Peng
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  3. Jianxin Li
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  5. Xiong Li
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Corresponding author

Correspondence to Jianxin Li.

Additional information

Yaopeng Liu is currently a MS degree candidate at the Beijing Advanced Innovation Center for Big Data and Brain Computing and State Key Laboratory of Software Development Environment in Beihang University (BUAA), China. His research interests include representation learning and data mining.

Hao Peng is currently a PhD candidate at the Beijing Advanced Innovation Center for Big Data and Brain Computing, and State Key Laboratory of Software Development Environment in Beihang University (BUAA), China. His research interests include representation learning, social network analysis, and text mining.

Jianxin Li is a professor at the Beijing Advanced Innovation Center for Big Data and Brain Computing, and the State Key Laboratory of Software Development Environment in Beihang University (BUAA), China. His current research interests include big data, distributed system, virtualization, trustworthy computing and network security.

Yangqiu Song is an assistant professor at the Department of Computer Science and Engineering, Hong Kong University of Science and Technology, China. His research interest is using machine learning and data mining techniques to extract and infer insightful knowledge from big data.

Xiong Li received the PhD degree in pattern recognition and intelligence system from Shanghai Jiao Tong University, China in 2013. He is currently a senior engineer in National Computer Network Emergency Response Technical Team, China. His research interests include hybrid generative discriminative learning and probabilistic graphical model.

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Liu, Y., Peng, H., Li, J. et al. Event detection and evolution in multi-lingual social streams. Front. Comput. Sci. 14, 145612 (2020). https://doi.org/10.1007/s11704-019-8201-6

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  • DOI: https://doi.org/10.1007/s11704-019-8201-6

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