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Machine Learning and Applications on...

ProQuest Information and Learning Co.

Machine Learning and Applications on Social Media Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Machine Learning and Applications on Social Media Data./
作者:	Kalyanam, Janani.
面頁冊數:	1 online resource (86 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355067903

Machine Learning and Applications on Social Media Data.
Kalyanam, Janani.

Machine Learning and Applications on Social Media Data. - 1 online resource (86 pages)

Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2017.

Includes bibliographical references

The emergence of social media and advances in mobile technology and internet has resulted in constant connectivity across users enabling them to post, share, and engage with content published on the web. Studying and learning from such data about users, and their engagement with content can give insights into the current and emerging trends in society. However, studying social media data comes with its own set of unique challenges. Social media data is highly unstructured because the content is not curated to adhere to any formal structure. This makes the process of analyzing the data challenging. Each message published on social media has Social media data is also highly volatile since huge volumes of data is generated every second. In this thesis, we propose machine learning based algorithms and methodologies to accommodate these challenges; and apply the algorithms to solve problems in domains of public health and journalism.

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

Mode of access: World Wide Web

ISBN: 9780355067903Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Machine Learning and Applications on Social Media Data.
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100 1 $a Kalyanam, Janani. $3 1184684
245 1 0 $a Machine Learning and Applications on Social Media Data.
264 0 $c 2017
300 $a 1 online resource (86 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
500 $a Adviser: Gert Lanckriet.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2017.
504 $a Includes bibliographical references
520 $a The emergence of social media and advances in mobile technology and internet has resulted in constant connectivity across users enabling them to post, share, and engage with content published on the web. Studying and learning from such data about users, and their engagement with content can give insights into the current and emerging trends in society. However, studying social media data comes with its own set of unique challenges. Social media data is highly unstructured because the content is not curated to adhere to any formal structure. This makes the process of analyzing the data challenging. Each message published on social media has Social media data is also highly volatile since huge volumes of data is generated every second. In this thesis, we propose machine learning based algorithms and methodologies to accommodate these challenges; and apply the algorithms to solve problems in domains of public health and journalism.
520 $a Chapter 1 proposes a new framework to combine the text and user engagement data to detect trends from social networks.
520 $a Chapter 2 studies social media data to predict the impact of news events. The chatter on social media surrounding news events is accurately quantified, and is found to be the most distinguishing feature between high-impact and low-impact events.
520 $a Chapter 3 uses topic modeling to discover attitudes and trends about drug abuse.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, San Diego. $b Electrical Engineering. $3 1184434
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10281183 $z click for full text (PQDT)

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