@inproceedings{3f77ef46308e494aa6c6f0703bbe26ed,
title = "Quantifying and bursting the online filter bubble",
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.",
keywords = "Controversy, Echo chambers, Filter bubble, Polarization, Social media",
author = "Kiran Garimella",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 10th ACM International Conference on Web Search and Data Mining, WSDM 2017 ; Conference date: 06-02-2017 Through 10-02-2017",
year = "2017",
month = feb,
day = "2",
doi = "10.1145/3018661.3024933",
language = "English (US)",
series = "WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining",
publisher = "Association for Computing Machinery, Inc",
pages = "837",
booktitle = "WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining",
}