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Understanding Social Media Influence, Semantic Network Analysis, and Thematic Campaign Campaign Classification Using Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Understanding Social Media Influence, Semantic Network Analysis, and Thematic Campaign Campaign Classification Using Machine Learning./
作者:	Johnson, Nathan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	139 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
標題:	Computer engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29165151
ISBN:	9798802716540

Understanding Social Media Influence, Semantic Network Analysis, and Thematic Campaign Campaign Classification Using Machine Learning.
Johnson, Nathan.

Understanding Social Media Influence, Semantic Network Analysis, and Thematic Campaign Campaign Classification Using Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 139 p.

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--Arizona State University, 2022.

This item must not be sold to any third party vendors.

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.

ISBN: 9798802716540Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Influence campaigns

Understanding Social Media Influence, Semantic Network Analysis, and Thematic Campaign Campaign Classification Using Machine Learning.
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