Quantifying and bursting the online filter bubble

Kiran Garimella

doi:10.1145/3018661.3024933

Quantifying and bursting the online filter bubble

Kiran Garimella^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-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 language	English
Title of host publication	WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining
Publisher	ACM
Number of pages	1
ISBN (Electronic)	9781450346757
DOIs	https://doi.org/10.1145/3018661.3024933
Publication status	Published - 2 Feb 2017
MoE publication type	A4 Article in a conference publication
Event	International Conference on Web Search & Data Mining - Cambridge, United Kingdom Duration: 6 Feb 2017 → 10 Feb 2017 Conference number: 10

Conference

Conference	International Conference on Web Search & Data Mining
Abbreviated title	WSDM
Country	United Kingdom
City	Cambridge
Period	06/02/2017 → 10/02/2017

Keywords

Controversy
Echo chambers
Filter bubble
Polarization
Social media

Access to Document

10.1145/3018661.3024933

Link to publication in Scopus

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Cite this

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