How can the search process on Twitter be improved to better meet the various information needs of its users? As an answer to this question, we have developed the Twinder framework, a scalable search system for Twitter streams. Twinder contains algorithms to determine the relevance of tweets in relation to search requests, as well as components to detect (near-)duplicate content, to diversify search results, and to personalize the search result ranking. In this paper, we report on our current progress, including the system architecture and the different modules for solving specific problems. Finally, we empirically determine the effectiveness of Twinder's components with experiments on representative datasets.

Original languageEnglish
Title of host publicationBridging Between Information Retrieval and Databases - PROMISE Winter School 2013, Revised Tutorial Lectures
PublisherSpringer Verlag
Number of pages10
Volume8173 LNCS
ISBN (Print)9783642547973
StatePublished - 2014
Event2013 PROMISE Winter School: Bridging Between Information Retrieval and Databases - Bressanone, Italy
Duration: 4 Feb 20138 Feb 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8173 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference2013 PROMISE Winter School: Bridging Between Information Retrieval and Databases

ID: 10683813