東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Managing Quality, Identity and Adver...

Brennan, Michael.

Linked to FindBook

Google Book

Amazon

博客來

Managing Quality, Identity and Adversaries in Public Discourse with Machine Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Managing Quality, Identity and Adversaries in Public Discourse with Machine Learning./
Author:	Brennan, Michael.
Description:	127 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-06(E), Section: B.
Contained By:	Dissertation Abstracts International74-06B(E).
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3536018
ISBN:	9781267909442

Managing Quality, Identity and Adversaries in Public Discourse with Machine Learning.
Brennan, Michael.

Managing Quality, Identity and Adversaries in Public Discourse with Machine Learning. - 127 p.

Source: Dissertation Abstracts International, Volume: 74-06(E), Section: B.

Thesis (Ph.D.)--Drexel University, 2012.

Automation can mitigate issues when scaling and managing quality and identity in public discourse on the web. Discourse needs to be curated and filtered. Anonymous speech has to be supported while handling adversaries. Reliance on human curators or analysts does not scale and content can be missed. These scaling and management issues include the limits of crowdsourced comment rating systems, flaws in crowdsourced topic identification systems, and the identification or anonymization of large numbers of authors. Scaling rating and topic identification systems results in missing relevant quality discourse. Authorship recognition without automation is time consuming, costly, and does not scale. Using machine learning to automate authorship recognition gives rise to serious privacy and anonymity concerns, and deception in writing style can be difficult to detect. This work replicates comment ratings and topic identification of crowdsourcing systems that currently rely on human participation to be effective. This work also demonstrates the ability of machine learning to identify authors quickly and accurately, as well as methods to circumvent this identification. Finally, this work presents novel data sets as a foundation for future research in comment classification and adversarial authorship recognition.

ISBN: 9781267909442Subjects--Topical Terms:

626642
Computer Science.

Managing Quality, Identity and Adversaries in Public Discourse with Machine Learning.
LDR:02220nmm a2200289 4500 001 2055885
005 20150319081952.5
008 170521s2012 ||||||||||||||||| ||eng d
020 $a 9781267909442
035 $a (MiAaPQ)AAI3536018
035 $a AAI3536018
040 $a MiAaPQ $c MiAaPQ
100 1 $a Brennan, Michael. $3 3169590
245 1 0 $a Managing Quality, Identity and Adversaries in Public Discourse with Machine Learning.
300 $a 127 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-06(E), Section: B.
500 $a Adviser: Rachel Greenstadt.
502 $a Thesis (Ph.D.)--Drexel University, 2012.
520 $a Automation can mitigate issues when scaling and managing quality and identity in public discourse on the web. Discourse needs to be curated and filtered. Anonymous speech has to be supported while handling adversaries. Reliance on human curators or analysts does not scale and content can be missed. These scaling and management issues include the limits of crowdsourced comment rating systems, flaws in crowdsourced topic identification systems, and the identification or anonymization of large numbers of authors. Scaling rating and topic identification systems results in missing relevant quality discourse. Authorship recognition without automation is time consuming, costly, and does not scale. Using machine learning to automate authorship recognition gives rise to serious privacy and anonymity concerns, and deception in writing style can be difficult to detect. This work replicates comment ratings and topic identification of crowdsourcing systems that currently rely on human participation to be effective. This work also demonstrates the ability of machine learning to identify authors quickly and accurately, as well as methods to circumvent this identification. Finally, this work presents novel data sets as a foundation for future research in comment classification and adversarial authorship recognition.
590 $a School code: 0065.
650 4 $a Computer Science. $3 626642
650 4 $a Information Science. $3 1017528
650 4 $a Web Studies. $3 1026830
690 $a 0984
690 $a 0723
690 $a 0646
710 2 $a Drexel University. $b Computer Science. $3 3169591
773 0 $t Dissertation Abstracts International $g 74-06B(E).
790 $a 0065
791 $a Ph.D.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3536018