Quantifying and bursting the online filter bubble

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this thesis, we develop methods to (i) detect and quantify the existence of filter bubbles in social media, (ii) monitor their evolution over time, and finally, (iii) devise methods to overcome the effects caused by filter bubbles. We are the first to propose an end-to-end system that solves the prob-lem of filter bubbles completely algorithmically. We build on top of existing studies and ideas from social science with principles from graph theory to design algorithms which are language independent, domain agnostic and scalable to large number of users.

Original languageEnglish (US)
Title of host publicationWSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages837
Number of pages1
ISBN (Electronic)9781450346757
DOIs
StatePublished - Feb 2 2017
Externally publishedYes
Event10th ACM International Conference on Web Search and Data Mining, WSDM 2017 - Cambridge, United Kingdom
Duration: Feb 6 2017Feb 10 2017

Publication series

NameWSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining

Other

Other10th ACM International Conference on Web Search and Data Mining, WSDM 2017
Country/TerritoryUnited Kingdom
CityCambridge
Period2/6/172/10/17

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems
  • Computer Networks and Communications
  • Software

Keywords

  • Controversy
  • Echo chambers
  • Filter bubble
  • Polarization
  • Social media

Fingerprint

Dive into the research topics of 'Quantifying and bursting the online filter bubble'. Together they form a unique fingerprint.

Cite this