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Uncovering Information Operations on Twitter Using Natural Language Processing and the Dynamic Wavelet Fingerprint.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Uncovering Information Operations on Twitter Using Natural Language Processing and the Dynamic Wavelet Fingerprint./
Author:	Kirn, Spencer Lee.
Description:	1 online resource (329 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 83-08, Section: B.
Contained By:	Dissertations Abstracts International83-08B.
Subject:	Applied mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28774693click for full text (PQDT)
ISBN:	9798780643630

Uncovering Information Operations on Twitter Using Natural Language Processing and the Dynamic Wavelet Fingerprint.
Kirn, Spencer Lee.

Uncovering Information Operations on Twitter Using Natural Language Processing and the Dynamic Wavelet Fingerprint. - 1 online resource (329 pages)

Source: Dissertations Abstracts International, Volume: 83-08, Section: B.

Thesis (Ph.D.)--The College of William and Mary, 2022.

Includes bibliographical references

Information Operations (IO) are campaigns waged by covert, powerful entities to distort public discourse in a direction that is advantageous for them. It is the behaviors of the underlying networks that signal these campaigns in action, not the specific content they are posting. In this dissertation we introduce a social media analysis system that uncovers these behaviors by analyzing the specific post timings of underlying accounts and networks. The presented method first clusters tweets based on content using Natural Language Processing (NLP). Each of these clusters - referred to as topics - are plotted in time using the attached metadata for each tweet. These topic signals are then analyzed using the Dynamic Wavelet Fingerprint (DWFP), which creates binary images of each topic that describe localized behaviors in the topic's propagation through Twitter. The features extracted from the DWFP and the underlying tweet metadata can be applied to various analyses. In this dissertation we present four applications of the presented method. First, we break down seven culturally significant tweet storms to identify characteristic, localized behavior that are common among and unique to each tweet storm. Next, we use the DWFP signal processing to identify bot accounts. Then this method is applied to a large dataset of tweets from the early weeks of the Covid-19 pandemic to identify densely connected communities, many of which display potential IO behaviors. Finally, this method is applied to a live-stream of Turkish tweets to identify coordinated networks working to push various agendas through a volatile time in Turkish politics.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798780643630Subjects--Topical Terms:

2122814
Applied mathematics.
Subjects--Index Terms:

Disinformation campaignsIndex Terms--Genre/Form:

542853
Electronic books.

Uncovering Information Operations on Twitter Using Natural Language Processing and the Dynamic Wavelet Fingerprint.
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504 $a Includes bibliographical references
520 $a Information Operations (IO) are campaigns waged by covert, powerful entities to distort public discourse in a direction that is advantageous for them. It is the behaviors of the underlying networks that signal these campaigns in action, not the specific content they are posting. In this dissertation we introduce a social media analysis system that uncovers these behaviors by analyzing the specific post timings of underlying accounts and networks. The presented method first clusters tweets based on content using Natural Language Processing (NLP). Each of these clusters - referred to as topics - are plotted in time using the attached metadata for each tweet. These topic signals are then analyzed using the Dynamic Wavelet Fingerprint (DWFP), which creates binary images of each topic that describe localized behaviors in the topic's propagation through Twitter. The features extracted from the DWFP and the underlying tweet metadata can be applied to various analyses. In this dissertation we present four applications of the presented method. First, we break down seven culturally significant tweet storms to identify characteristic, localized behavior that are common among and unique to each tweet storm. Next, we use the DWFP signal processing to identify bot accounts. Then this method is applied to a large dataset of tweets from the early weeks of the Covid-19 pandemic to identify densely connected communities, many of which display potential IO behaviors. Finally, this method is applied to a live-stream of Turkish tweets to identify coordinated networks working to push various agendas through a volatile time in Turkish politics.
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538 $a Mode of access: World Wide Web
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653 $a Network analysis
653 $a Twitter
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