國立虎尾科技大學 |

Investigating the Role of Silent Users on Social Media.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Investigating the Role of Silent Users on Social Media./
作者:	Karami, Mansooreh.
面頁冊數:	1 online resource (129 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: A.
Contained By:	Dissertations Abstracts International85-06A.
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798381157147

Investigating the Role of Silent Users on Social Media.
Karami, Mansooreh.

Investigating the Role of Silent Users on Social Media. - 1 online resource (129 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: A.

Thesis (Ph.D.)--Arizona State University, 2023.

Includes bibliographical references

Social media platforms provide a rich environment for analyzing user behavior. Recently, deep learning-based methods have been a mainstream approach for social media analysis models involving complex patterns. However, these methods are susceptible to biases in the training data, such as participation inequality. Basically, a mere 1% of users generate the majority of the content on social networking sites, while the remaining users, though engaged to varying degrees, tend to be less active in content creation and largely silent. These silent users consume and listen to information that is propagated on the platform. However, their voice, attitude, and interests are not reflected in the online content, making the decision of the current methods predisposed towards the opinion of the active users. So models can mistake the loudest users for the majority. To make the silent majority heard is to reveal the true landscape of the platform.In this dissertation, to compensate for this bias in the data, which is related to user-level data scarcity, I introduce three pieces of research work. Two of these proposed solutions deal with the data on hand while the other tries to augment the current data. Specifically, the first proposed approach modifies the weight of users' activity/interaction in the input space, while the second approach involves reweighting the loss based on the users' activity levels during the downstream task training. Lastly, the third approach uses large language models (LLMs) and learns the user's writing behavior to expand the current data. In other words, by utilizing LLMs as a sophisticated knowledge base, this method aims to augment the silent user's data.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381157147Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Social media analysisIndex Terms--Genre/Form:

554714
Electronic books.

Investigating the Role of Silent Users on Social Media.
LDR:03080ntm a22004097 4500 001 1149569
005 20241022112134.5
006 m o d
007 cr bn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798381157147
035 $a (MiAaPQ)AAI30687818
035 $a AAI30687818
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Karami, Mansooreh. $3 1475862
245 1 0 $a Investigating the Role of Silent Users on Social Media.
264 0 $c 2023
300 $a 1 online resource (129 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: Dissertations Abstracts International, Volume: 85-06, Section: A.
500 $a Advisor: Liu, Huan.
502 $a Thesis (Ph.D.)--Arizona State University, 2023.
504 $a Includes bibliographical references
520 $a Social media platforms provide a rich environment for analyzing user behavior. Recently, deep learning-based methods have been a mainstream approach for social media analysis models involving complex patterns. However, these methods are susceptible to biases in the training data, such as participation inequality. Basically, a mere 1% of users generate the majority of the content on social networking sites, while the remaining users, though engaged to varying degrees, tend to be less active in content creation and largely silent. These silent users consume and listen to information that is propagated on the platform. However, their voice, attitude, and interests are not reflected in the online content, making the decision of the current methods predisposed towards the opinion of the active users. So models can mistake the loudest users for the majority. To make the silent majority heard is to reveal the true landscape of the platform.In this dissertation, to compensate for this bias in the data, which is related to user-level data scarcity, I introduce three pieces of research work. Two of these proposed solutions deal with the data on hand while the other tries to augment the current data. Specifically, the first proposed approach modifies the weight of users' activity/interaction in the input space, while the second approach involves reweighting the loss based on the users' activity levels during the downstream task training. Lastly, the third approach uses large language models (LLMs) and learns the user's writing behavior to expand the current data. In other words, by utilizing LLMs as a sophisticated knowledge base, this method aims to augment the silent user's data.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 569006
650 4 $a Computer science. $3 573171
650 4 $a Web studies. $3 1148502
653 $a Social media analysis
653 $a Deep learning
653 $a Social networking
653 $a Large language models
653 $a Social networking
655 7 $a Electronic books. $2 local $3 554714
690 $a 0464
690 $a 0984
690 $a 0646
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Arizona State University. $b Computer Engineering. $3 1184417
773 0 $t Dissertations Abstracts International $g 85-06A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30687818 $z click for full text (PQDT)