國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Multicultural Analysis of Topic and Emotion Drift on YouTube's Recommendation System./
作者:	Onyepunuka, Ugochukwu Olaitan Peter.
面頁冊數:	1 online resource (42 pages)
附註:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
標題:	Information science. -
電子資源:	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.
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