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A Quantative Study of Social Media Echo Chambers
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
2018 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The changing online environment - where the breadth of the information we are exposed to is algorithmically narrowed - has raised concerns about the creation of "echo chambers"; in which individuals are exposed mainly to information already in alignment with their preconceived ideas and opinions.

This thesis explores the role of Twitter as a social media and as an information network, and investigates if exposure to and participation in political discussions resembles echo chambers. The findings by analyzing the Twitter friendship network shows that users on Twitter tend to prefer to follow like-minded individuals to some extent, but not in the domain of what constitutes an echo chamber. However, analyzing all communication events associated to a set of influential politicians, newspapers, journalists and bloggers during a ten-day period in connection to the British general election in June 2017, reveals that users are more likely to engage with ideologically similar peers, than with users with different political beliefs from themselves.

Data is collected using both the Twitter rest and streaming API, and is analyzed using methods from graph theory and social network analysis. One part of the project involves collection and analysis of several million tweets, in which case the cluster computing platform Apache Spark is used. The other part is concerned with finding the degrees of separation between accounts in the Twitter network, in which case the API is queried step-by-step.

Place, publisher, year, edition, pages
2018.
Series
UPTEC F, ISSN 1401-5757 ; 17059
National Category
Mathematics Computer and Information Sciences
Identifiers
URN: urn:nbn:se:uu:diva-339898OAI: oai:DiVA.org:uu-339898DiVA, id: diva2:1176971
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2018-01-24 Created: 2018-01-23 Last updated: 2018-04-09Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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