國立虎尾科技大學 |

Multicultural Analysis of Topic and Emotion Drift on YouTube's Recommendation System.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Multicultural Analysis of Topic and Emotion Drift on YouTube's Recommendation System./
Author:	Onyepunuka, Ugochukwu Olaitan Peter.
Description:	1 online resource (42 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
Subject:	Information science. -
Online resource:	click for full text (PQDT)
ISBN:	9798379504762

Multicultural Analysis of Topic and Emotion Drift on YouTube's Recommendation System.
Onyepunuka, Ugochukwu Olaitan Peter.

Multicultural Analysis of Topic and Emotion Drift on YouTube's Recommendation System. - 1 online resource (42 pages)

Source: Masters Abstracts International, Volume: 84-11.

Thesis (M.S.)--University of Arkansas at Little Rock, 2023.

Includes bibliographical references

YouTube serves as a primary information source for many users, with its recommendation algorithm playing a vital role in video discovery and viewership on the platform. It determines what users are exposed to and is responsible for a significant portion (70%) of the content users engage with on the platform. It is therefore crucial to scrutinize recommendation systems to understand potential algorithmic biases that may spread disinformation. Previous studies have shown that the recommendation algorithm favors a small number of videos, creating mild ideological echo chambers. This study aims to investigate the extent to which YouTube's recommendation algorithm spreads disinformation by analyzing the Cheng Ho narrative. Cheng Ho was a Chinese Muslim naval admiral in the 15th century, known as the "Chinese Columbus," and symbolized China's peaceful ascendancy to power. To achieve this aim, a list of 50 videos on Cheng Ho was gathered by using relevant keywords with YouTube's search API. These videos served as the seed for recommendations, with 58,825 unique videos collected through five depths of recommendations. To determine the relevance of the recommendations to the Cheng Ho narrative, I computed the topic drift on the recommendation depths and discovered that the recommendations led us further away from the original topic. Furthermore, observing the eigenvector centrality values of videos within the recommendation network of different depths, I saw the evolution of influential videos as their relevance to Cheng Ho diminished. The results showed how YouTube's recommendation system discards the topics of the seed videos by subtly introducing a new but still pro-China topic in the network through influential videos. This new topic is about economic growth and religious freedom in China targeting Indonesia's younger demographic by focusing on current events and pop culture. This study sets the stage for further research in analyzing bias in recommendation algorithms, their exploitation by information actors, their impact on mis/disinformation propagation, and their effect on user consumption.

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

Mode of access: World Wide Web

ISBN: 9798379504762Subjects--Topical Terms:

561178
Information science.
Subjects--Index Terms:

YouTube's recommendationIndex Terms--Genre/Form:

554714
Electronic books.

Multicultural Analysis of Topic and Emotion Drift on YouTube's Recommendation System.
LDR:03452ntm a22003737 4500 001 1142605
005 20240422071028.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379504762
035 $a (MiAaPQ)AAI30487367
035 $a AAI30487367
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Onyepunuka, Ugochukwu Olaitan Peter. $3 1466993
245 1 0 $a Multicultural Analysis of Topic and Emotion Drift on YouTube's Recommendation System.
264 0 $c 2023
300 $a 1 online resource (42 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: Masters Abstracts International, Volume: 84-11.
500 $a Advisor: Agarwal, Nitin.
502 $a Thesis (M.S.)--University of Arkansas at Little Rock, 2023.
504 $a Includes bibliographical references
520 $a YouTube serves as a primary information source for many users, with its recommendation algorithm playing a vital role in video discovery and viewership on the platform. It determines what users are exposed to and is responsible for a significant portion (70%) of the content users engage with on the platform. It is therefore crucial to scrutinize recommendation systems to understand potential algorithmic biases that may spread disinformation. Previous studies have shown that the recommendation algorithm favors a small number of videos, creating mild ideological echo chambers. This study aims to investigate the extent to which YouTube's recommendation algorithm spreads disinformation by analyzing the Cheng Ho narrative. Cheng Ho was a Chinese Muslim naval admiral in the 15th century, known as the "Chinese Columbus," and symbolized China's peaceful ascendancy to power. To achieve this aim, a list of 50 videos on Cheng Ho was gathered by using relevant keywords with YouTube's search API. These videos served as the seed for recommendations, with 58,825 unique videos collected through five depths of recommendations. To determine the relevance of the recommendations to the Cheng Ho narrative, I computed the topic drift on the recommendation depths and discovered that the recommendations led us further away from the original topic. Furthermore, observing the eigenvector centrality values of videos within the recommendation network of different depths, I saw the evolution of influential videos as their relevance to Cheng Ho diminished. The results showed how YouTube's recommendation system discards the topics of the seed videos by subtly introducing a new but still pro-China topic in the network through influential videos. This new topic is about economic growth and religious freedom in China targeting Indonesia's younger demographic by focusing on current events and pop culture. This study sets the stage for further research in analyzing bias in recommendation algorithms, their exploitation by information actors, their impact on mis/disinformation propagation, and their effect on user consumption.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Information science. $3 561178
653 $a YouTube's recommendation
653 $a Algorithms
653 $a Influential videos
653 $a Viewership
653 $a Economic growth
655 7 $a Electronic books. $2 local $3 554714
690 $a 0723
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Arkansas at Little Rock. $b Information Science. $3 1180965
773 0 $t Masters Abstracts International $g 84-11.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30487367 $z click for full text (PQDT)