Incremental clustering with vector expansion for online event detection in microblogs

Date

2017-11-04

Author

Ozdikis, Ozer
Karagöz, Pınar
Oğuztüzün, Mehmet Halit S.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Identifying similarities in microblog posts for event detection poses challenges due to short texts with idiosyncratic spellings, irregular writing styles, abbreviations and synonyms. In order to overcome these challenges, we present an enhancement to the incremental clustering techniques by detecting similar terms in microblog posts in a temporal context. We devise an unsupervised method to measure the similarities online using co-occurrence-based techniques and use them in a vector expansion process. The results of our evaluation performed on a tweet set indicate that the proposed vector expansion method helps identify similarities in tweets despite differences in their content. This facilitates the clustering of tweets and detection of events with higher accuracy without incurring a high execution cost.

Subject Keywords

Online event detection, Clustering, Vector expansion, Statistical text analysis, Microblogs

URI

https://hdl.handle.net/11511/33056

Journal

SOCIAL NETWORK ANALYSIS AND MINING

DOI

https://doi.org/10.1007/s13278-017-0476-8

Collections

Department of Computer Engineering, Article

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Citation Formats

O. Ozdikis, P. Karagöz, and M. H. S. Oğuztüzün, “Incremental clustering with vector expansion for online event detection in microblogs,” SOCIAL NETWORK ANALYSIS AND MINING, pp. 0–0, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/33056.