Full metadata
Title
Understanding Social Media Influence, Semantic Network Analysis, and Thematic Campaign Campaign Classification Using Machine Learning.
Description
Individuals and organizations have greater access to the world's population than ever before. The effects of Social Media Influence have already impacted the behaviour and actions of the world's population. This research employed mixed methods to investigate the mechanisms to further the understand of how Social Media Influence Campaigns (SMIC) impact the global community as well as develop tools and frameworks to conduct analysis. The research has qualitatively examined the perceptions of Social Media, specifically how leadership believe it will change and it's role within future conflict. This research has developed and tested semantic ontological modelling to provide insights into the nature of network related behaviour of SMICs. This research also developed exemplar data sets of SMICs. The insights gained from initial research were used to train Machine Learning classifiers to identify thematically related campaigns. This work has been conducted in close collaboration with Alliance Plus Network partner, University of New South Wales and the Australian Defence Force.
Date Created
2022
Contributors
- Johnson, Nathan (Author)
- Reisslein, Martin (Thesis advisor)
- Turnbull, Benjamin (Committee member)
- Zhao, Ming (Committee member)
- Zhang, Yanchao (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
138 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.171644
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: Ph.D., Arizona State University, 2022
Field of study: Computer Engineering
System Created
- 2022-12-20 06:19:18
System Modified
- 2022-12-20 06:19:18
- 1 year 10 months ago
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