Twibo: comparing very large communities via massive social media datasets

Xia, Tian; Chen, Miao; Liu, Xiaozhong

Permalink

https://hdl.handle.net/2142/89434 Copy

Description

Title

Twibo: comparing very large communities via massive social media datasets

Author(s)

Xia, Tian; Chen, Miao; Liu, Xiaozhong

Issue Date

2016-03-15

Keyword(s)

• Social media
• computational social science
• community comparison
• Twitter
• Weibo

Abstract

Online social media are becoming the standard infrastructure for social communication and dissemination of information. As social media platforms not only passively provide infrastructure but also actively perform algorithmic curation for their profit and user experience, an important concern, often called ``filter bubble'' arises: people are trapped in their own personalized bubble--being exposed only to the opinions that conform their beliefs and political positions, thus potentially creating social polarization and information ``islands''. Although the adoption of social media is an international phenomenon, language difference and policy barrier also create information islands. The goal of this paper is to develop methods/system to cross-link concepts and communities in different social media, and leverage them to study the extent and impact of filter bubbles. To accomplish this goal, the main objectives in this paper are to develop text/graph mining methods to connect concepts and entities in Twitter and Weibo through Wikipedia; and to compare two social media in the dimension of topics and networks to quantify the significance of language bubble.

Publisher

iSchools

Series/Report Name or Number

IConference 2016 Proceedings

Type of Resource

text

Language

eng

Permalink

http://hdl.handle.net/2142/89434

DOI

https://doi.org/10.9776/16148

Copyright and License Information

Copyright 2016 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.

Owning Collections