Event detection and evolution in multi-lingual social streams

• Published in: Frontiers of Computer Science Vol. 14; no. 5; p. 145612
• Main Authors: LIU, Yaopeng, PENG, Hao, LI, Jianxin, SONG, Yangqiu, LI, Xiong
• Format: Journal Article
• Language: English
• Published: Beijing Higher Education Press 01.10.2020; Springer Nature B.V
• Subjects: Computer Science; Correlation analysis; Earthquakes; event detection; event evolution; Evolution; Keywords; multi-lingual anomaly detection; Multilingualism; Research Article; Semantics; stream processing; multi-lingual anomaly detection; stream processing; event detection; event evolution
• ISSN: 2095-2228; 2095-2236
• DOI: 10.1007/s11704-019-8201-6

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 humangenerated dataset containing real world events. Experimental results show that our new model MLEM outperforms the baseline method both in efficiency and effectiveness.