2026-07-21T11:21:33Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:4569 2024-03-06T10:29:31Z ddc:004 open_access

Detecting a Tweet’s Topic within a Large Number of Portuguese Twitter Trends Rosa, Hugo Carvalho, João Paulo Batista, Fernando topic detection social networks data mining Twitter Portuguese language In this paper we propose to approach the subject of Twitter Topic Detection when in the presence of a large number of trending topics. We use a new technique, called Twitter Topic Fuzzy Fingerprints, and compare it with two popular text classification techniques, Support Vector Machines (SVM) and k-Nearest Neighbours (kNN). Preliminary results show that it outperforms the other two techniques, while still being much faster, which is an essential feature when processing large volumes of streaming data. We focused on a data set of Portuguese language tweets and the respective top trends as indicated by Twitter. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Hugo Rosa and João Paulo Carvalho and Fernando Batista 2014 Is Part Of OASIcs, Volume 38, 3rd Symposium on Languages, Applications and Technologies (2014) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.SLATE.2014.185 urn:nbn:de:0030-drops-45696 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2014.185 eng https://creativecommons.org/licenses/by/3.0/legalcode