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

Inferring Degree of Localization of ...

Panasyuk, Aleksey Valeriy.

FindBook

Google Book

Amazon

博客來

Inferring Degree of Localization of Twitter Persons and Topics through Time, Language, and Location Features.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Inferring Degree of Localization of Twitter Persons and Topics through Time, Language, and Location Features./
作者:	Panasyuk, Aleksey Valeriy.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	178 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28321696
ISBN:	9798516958328

Inferring Degree of Localization of Twitter Persons and Topics through Time, Language, and Location Features.
Panasyuk, Aleksey Valeriy.

Inferring Degree of Localization of Twitter Persons and Topics through Time, Language, and Location Features. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 178 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--Syracuse University, 2021.

This item must not be sold to any third party vendors.

Identifying authoritative influencers related to a geographic area (geo-influencers) can aid content recommendation systems and local expert finding. This thesis addresses this important problem using Twitter data.A geo-influencer is identified via the locations of its followers. On Twitter, due to privacy reasons, the location reported by followers is limited to profile via a textual string or messages with coordinates. However, this textual string is often not possible to geocode and less than 1\\% of message traffic provides coordinates. First, the error rates associated with Google's geocoder are studied and a classifier is built that gives a warning for self-reported locations that are likely incorrect. Second, it is shown that city-level geo-influencers can be identified without geocoding by leveraging the power of Google search and follower-followee network structure. Third, we illustrate that the global vs. local influencer, at the timezone level, can be identified using a classifier using the temporal features of the followers. For global influencers, spatiotemporal analysis helps understand the evolution of their popularity over time. When applied over message traffic, the approach can differentiate top trending topics and persons in different geographical regions. Fourth, we constrain a timezone to a set of possible countries and use language features for training a high-level geocoder to further localize an influencer's geographic area. Finally, we provide a repository of geo-influencers for applications related to content recommendation. The repository can be used for filtering influencers based on their audience's demographics related to location, time, language, gender, and ethnicity.

ISBN: 9798516958328Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Account creation time distribution

Inferring Degree of Localization of Twitter Persons and Topics through Time, Language, and Location Features.
LDR:03191nmm a2200457 4500 001 2285176
005 20211129123958.5
008 220723s2021 ||||||||||||||||| ||eng d
020 $a 9798516958328
035 $a (MiAaPQ)AAI28321696
035 $a AAI28321696
040 $a MiAaPQ $c MiAaPQ
100 1 $a Panasyuk, Aleksey Valeriy. $3 3564461
245 1 0 $a Inferring Degree of Localization of Twitter Persons and Topics through Time, Language, and Location Features.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 178 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Mohan, Chilukuri K.;Mehrotra, Kishan G.
502 $a Thesis (Ph.D.)--Syracuse University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Identifying authoritative influencers related to a geographic area (geo-influencers) can aid content recommendation systems and local expert finding. This thesis addresses this important problem using Twitter data.A geo-influencer is identified via the locations of its followers. On Twitter, due to privacy reasons, the location reported by followers is limited to profile via a textual string or messages with coordinates. However, this textual string is often not possible to geocode and less than 1\\% of message traffic provides coordinates. First, the error rates associated with Google's geocoder are studied and a classifier is built that gives a warning for self-reported locations that are likely incorrect. Second, it is shown that city-level geo-influencers can be identified without geocoding by leveraging the power of Google search and follower-followee network structure. Third, we illustrate that the global vs. local influencer, at the timezone level, can be identified using a classifier using the temporal features of the followers. For global influencers, spatiotemporal analysis helps understand the evolution of their popularity over time. When applied over message traffic, the approach can differentiate top trending topics and persons in different geographical regions. Fourth, we constrain a timezone to a set of possible countries and use language features for training a high-level geocoder to further localize an influencer's geographic area. Finally, we provide a repository of geo-influencers for applications related to content recommendation. The repository can be used for filtering influencers based on their audience's demographics related to location, time, language, gender, and ethnicity.
590 $a School code: 0659.
650 4 $a Computer science. $3 523869
650 4 $a Information technology. $3 532993
650 4 $a Multimedia communications. $3 590562
650 4 $a Information science. $3 554358
650 4 $a Research. $3 531893
650 4 $a Language. $3 643551
650 4 $a Personal information. $3 3562412
650 4 $a Datasets. $3 3541416
650 4 $a Application programming interface. $3 3562904
650 4 $a Trends. $3 3560051
650 4 $a Multilingualism. $3 598147
650 4 $a Ethnicity. $3 519288
650 4 $a Localization. $3 3560711
650 4 $a Queries. $3 3564462
650 4 $a Keywords. $3 3560140
650 4 $a Popularity. $3 3564342
650 4 $a Cities. $3 3544022
653 $a Account creation time distribution
653 $a Global influence characterization
653 $a Local expert finding
653 $a Network dynamics
653 $a Social networks
653 $a Temporal features
653 $a Degree of localization
653 $a Twitter topics
653 $a Twitter people
653 $a Geo-influencer
690 $a 0984
690 $a 0489
690 $a 0558
690 $a 0723
690 $a 0679
710 2 $a Syracuse University. $b Electrical Engineering and Computer Science. $3 3169988
773 0 $t Dissertations Abstracts International $g 83-02B.
790 $a 0659
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28321696