Examining Untempered Social Media: Information Mutation on GAB.ai

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Muppalla, S. E. P. (2018). Examining Untempered Social Media: Information Mutation on GAB.ai. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Online social media often mirrors the social phenomenon of "Chinese Whispers" where information mutates during dissemination.Many a time, Twitter and Facebook in conjunction with smaller "fringe" communities like Gab.ai, often echo perspectives to facts and many do so under the guise of "free speech". In this work, we propose a novel framework to examine the information mutation and in some cases online extremism using approximately 43 million posts (both original content and comments) from 450,000 users on Gab in conjunction with 3 million related blogs and news articles. We develop a system that mines the interaction between Gab and mainstream news media, when there is discourse commonality (for eg. during a shock event - say Unite the Right protests in Charlottesville during August 2017) and show evidence of how information mutates to the tune of the echoes within the Gab social system. To demonstrate our framework, we present two case studies of information mutation and online extremism, namely - the Charlottesville Unite the Right protests and the Pittsburgh synagogue shooting in October 2018. Particularly, in the context of the Pittsburgh shooting, we present a thorough analysis of content similar to that of the shooter and evolving narratives of distorted information.

Details
Author

Muppalla, Sai Eshwar Prasad

Title

Examining Untempered Social Media: Information Mutation on GAB.ai

Physical Description

1 online resource (62 pages) : PDF

Date

2018

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Online social media often mirrors the social phenomenon of "Chinese Whispers" where information mutates during dissemination.Many a time, Twitter and Facebook in conjunction with smaller "fringe" communities like Gab.ai, often echo perspectives to facts and many do so under the guise of "free speech". In this work, we propose a novel framework to examine the information mutation and in some cases online extremism using approximately 43 million posts (both original content and comments) from 450,000 users on Gab in conjunction with 3 million related blogs and news articles. We develop a system that mines the interaction between Gab and mainstream news media, when there is discourse commonality (for eg. during a shock event - say Unite the Right protests in Charlottesville during August 2017) and show evidence of how information mutates to the tune of the echoes within the Gab social system. To demonstrate our framework, we present two case studies of information mutation and online extremism, namely - the Charlottesville Unite the Right protests and the Pittsburgh synagogue shooting in October 2018. Particularly, in the context of the Pittsburgh shooting, we present a thorough analysis of content similar to that of the shooter and evolving narratives of distorted information.

Genre

masters theses

Subjects--Topics

Computer science

Degree

M.S.

Keywords

Clustering
gab.ai
Information Mutation
Natural Language Processing
Story Chaining
Text Similarity

Subject Area

Computer Science

Advisor(s)

Krishnan, Siddharth

Committee Members

Cukic, Bojan
Lee, Minwoo

Degree Note

Thesis (M.S.)--University of North Carolina at Charlotte, 2018.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Muppalla_uncc_0694N_11946

Permalink

http://hdl.handle.net/20.500.13093/etd:975