Quantifying and bursting the online filter bubble

Kiran Garimella*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)

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
Title of host publicationWSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining
PublisherACM
Number of pages1
ISBN (Electronic)9781450346757
DOIs
Publication statusPublished - 2 Feb 2017
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Web Search & Data Mining - Cambridge, United Kingdom
Duration: 6 Feb 201710 Feb 2017
Conference number: 10

Conference

ConferenceInternational Conference on Web Search & Data Mining
Abbreviated titleWSDM
CountryUnited Kingdom
CityCambridge
Period06/02/201710/02/2017

Keywords

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

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