Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/16047
Author(s): | Carvalho, J. P. Rosa, H. Batista, F. |
Date: | 2017 |
Title: | Detecting relevant tweets in very large tweet collections: the London Riots case study |
Event title: | 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 |
ISSN: | 1098-7584 |
ISBN: | 978-1-5090-6034-4 |
DOI (Digital Object Identifier): | 10.1109/FUZZ-IEEE.2017.8015635 |
Keywords: | Twitter Fingerprint recognition Market research Tagging Libraries Electronic mail Databases |
Abstract: | In this paper we propose to approach the subject of detecting relevant tweets when in the presence of very large tweet collections containing a large number of different trending topics. We use a large database of tweets collected during the 2011 London Riots as a case study to demonstrate the application of the proposed techniques. In order to extract relevant content, we extend, formalize and apply a recent technique, called Twitter Topic Fuzzy Fingerprints, which, in the scope of social media, outperforms other well known text based classification methods, while being less computationally demanding, an essential feature when processing large volumes of streaming data. Using this technique we were able to detect 45% additional relevant tweets within the database. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | CTI-CRI - Comunicações a conferências internacionais |
Files in This Item:
File | Description | Size | Format | |
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Carvalho 2017a - Detecting relevant tweets in very large tweet collections the London Riots case study.pdf | Pós-print | 275,51 kB | Adobe PDF | View/Open |
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