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Spam, Fraud, and Bots : = Improving the Integrity of Online Social Media Data.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Spam, Fraud, and Bots :/
Reminder of title:	Improving the Integrity of Online Social Media Data.
Author:	Minnich, Amanda Jean.
Description:	1 online resource (104 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:	Dissertation Abstracts International79-04B(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9780355326895

Spam, Fraud, and Bots : = Improving the Integrity of Online Social Media Data.
Minnich, Amanda Jean.

Spam, Fraud, and Bots :Improving the Integrity of Online Social Media Data. - 1 online resource (104 pages)

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.

Thesis (Ph.D.)--The University of New Mexico, 2017.

Includes bibliographical references

Online data contains a wealth of information, but as with most user-generated content, it is full of noise, fraud, and automated behavior. The prevalence of "junk" and fraudulent text affects users, businesses, and researchers alike. To make matters worse, there is a lack of ground truth data for these types of text, and the appearance of the text is constantly changing as fraudsters adapt to pressures from hosting sites. The goal of my dissertation is therefore to extract high-quality content from and identify fraudulent and automated behavior in large, complex social media datasets in the absence of ground truth data. Specifically, in my dissertation I design a collection of data inspection, filtering, fusion, mining, and exploration algorithms to: automate data cleaning to produce usable data for mining algorithms, quantify the trustworthiness of business behavior in online e-commerce sites, and efficiently identify automated accounts in large and constantly changing social networks. The main components of this work include: noise removal, data fusion, multi-source feature generation, network exploration, and anomaly detection.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355326895Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Spam, Fraud, and Bots : = Improving the Integrity of Online Social Media Data.
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500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
500 $a Adviser: Abdullah Mueen.
502 $a Thesis (Ph.D.)--The University of New Mexico, 2017.
504 $a Includes bibliographical references
520 $a Online data contains a wealth of information, but as with most user-generated content, it is full of noise, fraud, and automated behavior. The prevalence of "junk" and fraudulent text affects users, businesses, and researchers alike. To make matters worse, there is a lack of ground truth data for these types of text, and the appearance of the text is constantly changing as fraudsters adapt to pressures from hosting sites. The goal of my dissertation is therefore to extract high-quality content from and identify fraudulent and automated behavior in large, complex social media datasets in the absence of ground truth data. Specifically, in my dissertation I design a collection of data inspection, filtering, fusion, mining, and exploration algorithms to: automate data cleaning to produce usable data for mining algorithms, quantify the trustworthiness of business behavior in online e-commerce sites, and efficiently identify automated accounts in large and constantly changing social networks. The main components of this work include: noise removal, data fusion, multi-source feature generation, network exploration, and anomaly detection.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
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