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Title:Unveiling Polarization in Social Networks: A Matrix Factorization Approach
Author(s):Amin, Md Tanvir Al; Aggarwal, Charu; Yao, Shuochao; Abdelzaher, Tarek F.; Kaplan, Lance
Matrix Factorization
Social Sensing
Gradient Descent Algorithm
Abstract:This paper presents unsupervised algorithms to uncover polarization in social networks (namely, Twitter) and identify polarized groups. The approach is language-agnostic and thus broadly applicable to global and multilingual media. In cases of conflict, dispute, or situations involving multiple parties with contrasting interests, opinions get divided into different camps. Previous manual inspection of tweets has shown that such situations produce distinguishable signatures on Twitter, as people take sides leading to clusters that preferentially propagate information confirming their individual cluster-specific bias. We propose a model for polarized social networks, and show that approaches based on factorizing the matrix of sources and their claims can automate the discovery of polarized clusters with no need for prior training or natural language processing. In turn, identifying such clusters offers insights into prevalent social conflicts and helps automate the generation of less biased descriptions of ongoing events. We evaluate our factorization algorithms and their results on multiple Twitter datasets involving polarization of opinions, demonstrating the efficacy of our approach. Experiments show that our method is almost always correct in identifying the polarized information from real-world twitter traces, and outperforms the baseline mechanisms by a large margin.
Issue Date:2017
Citation Info:Md Tanvir Al Amin, Charu Aggarwal, Shuochao Yao, Tarek Abdelzaher, Lance Kaplan, "Unveiling Polarization in Social Networks: A Matrix Factorization Approach", IEEE International Conference on Computer Communications, INFOCOM 2017, Atlanta, GA, USA, May 2017.
Genre:Technical Report
Conference Paper / Presentation
Sponsor:Army Research Laboratory, Cooperative Agreement W911NF-09-2-0053
DTRA grant HDTRA1-10-1-0120
NSF grant CNS 13-29886
Date Available in IDEALS:2017-01-13

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