Twinder: Enhancing Twitter Search

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


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
Subtitle of host publicationRevised Tutorial Lectures
EditorsNicola Ferro
Place of PublicationBerlin
Number of pages10
ISBN (Electronic)978-3-642-54798-0
ISBN (Print)978-3-642-54797-3
Publication statusPublished - 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)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2013 PROMISE Winter School: Bridging Between Information Retrieval and Databases


  • Exact Copy
  • Relevance Estimation
  • Retrieval Score
  • Cloud Computing Infrastructure
  • Microblog Post


Dive into the research topics of 'Twinder: Enhancing Twitter Search'. Together they form a unique fingerprint.

Cite this