東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Discovering and Mitigating Social Da...

Morstatter, Fred.

FindBook

Google Book

Amazon

博客來

Discovering and Mitigating Social Data Bias.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Discovering and Mitigating Social Data Bias./
作者:	Morstatter, Fred.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	187 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
標題:	Artificial intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10603064
ISBN:	9780355116984

Discovering and Mitigating Social Data Bias.
Morstatter, Fred.

Discovering and Mitigating Social Data Bias. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 187 p.

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)--Arizona State University, 2017.

Exabytes of data are created online every day. This deluge of data is no more apparent than it is on social media. Naturally, finding ways to leverage this unprecedented source of human information is an active area of research. Social media platforms have become laboratories for conducting experiments about people at scales thought unimaginable only a few years ago.

ISBN: 9780355116984Subjects--Topical Terms:

516317
Artificial intelligence.

Discovering and Mitigating Social Data Bias.
LDR:03178nmm a2200337 4500 001 2126970
005 20171128112459.5
008 180830s2017 ||||||||||||||||| ||eng d
020 $a 9780355116984
035 $a (MiAaPQ)AAI10603064
035 $a AAI10603064
040 $a MiAaPQ $c MiAaPQ
100 1 $a Morstatter, Fred. $3 2056661
245 1 0 $a Discovering and Mitigating Social Data Bias.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 187 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Adviser: Huan Liu.
502 $a Thesis (Ph.D.)--Arizona State University, 2017.
520 $a Exabytes of data are created online every day. This deluge of data is no more apparent than it is on social media. Naturally, finding ways to leverage this unprecedented source of human information is an active area of research. Social media platforms have become laboratories for conducting experiments about people at scales thought unimaginable only a few years ago.
520 $a Researchers and practitioners use social media to extract actionable patterns such as where aid should be distributed in a crisis. However, the validity of these patterns relies on having a representative dataset. As this dissertation shows, the data collected from social media is seldom representative of the activity of the site itself, and less so of human activity. This means that the results of many studies are limited by the quality of data they collect.
520 $a The finding that social media data is biased inspires the main challenge addressed by this thesis. I introduce three sets of methodologies to correct for bias. First, I design methods to deal with data collection bias. I offer a methodology which can find bias within a social media dataset. This methodology works by comparing the collected data with other sources to find bias in a stream. The dissertation also outlines a data collection strategy which minimizes the amount of bias that will appear in a given dataset. It introduces a crawling strategy which mitigates the amount of bias in the resulting dataset. Second, I introduce a methodology to identify bots and shills within a social media dataset. This directly addresses the concern that the users of a social media site are not representative. Applying these methodologies allows the population under study on a social media site to better match that of the real world. Finally, the dissertation discusses perceptual biases, explains how they affect analysis, and introduces computational approaches to mitigate them.
520 $a The results of the dissertation allow for the discovery and removal of different levels of bias within a social media dataset. This has important implications for social media mining, namely that the behavioral patterns and insights extracted from social media will be more representative of the populations under study.
590 $a School code: 0010.
650 4 $a Artificial intelligence. $3 516317
650 4 $a Computer science. $3 523869
650 4 $a Engineering. $3 586835
690 $a 0800
690 $a 0984
690 $a 0537
710 2 $a Arizona State University. $b Computer Science. $3 1676136
773 0 $t Dissertation Abstracts International $g 79-01B(E).
790 $a 0010
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10603064