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Extracting Narratives Using GPT-3 From Youtube Videos and Its Visualization.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Extracting Narratives Using GPT-3 From Youtube Videos and Its Visualization./
Author:	Gurung, Mayor Inna.
Description:	1 online resource (38 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
Subject:	Information science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30491963click for full text (PQDT)
ISBN:	9798379544751

Extracting Narratives Using GPT-3 From Youtube Videos and Its Visualization.
Gurung, Mayor Inna.

Extracting Narratives Using GPT-3 From Youtube Videos and Its Visualization. - 1 online resource (38 pages)

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

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

Includes bibliographical references

YouTube, the leading video-sharing social media platform, is home to a vast number of videos, with over one billion hours of content consumed daily. However, with 500 hours of fresh content added to the platform every day, ensuring the quality of the content has become a daunting task, making it challenging to prevent the spread of low-quality information. The manual review of malicious content by moderators is time-consuming and highlights the need for faster analysis and processing tools.This paper proposes a novel approach that utilizes Generative Pre-Trained Transfer-3 (GPT-3), with 175 billion parameters, to extract narratives from YouTube videos. The proposed model achieved a semantic similarity of 70% with state-of-the-art frameworks designed for narrative extraction. Furthermore, the study addresses the issue of content exploration on YouTube, as viewers tend to be recommended similar videos by the platform's algorithm. To address this issue, we propose the integration of the extracted narratives into a web-based visualization tool that enables users to explore different variations of the same or combined keywords. The proposed tool aims to enhance the quality of the content and provide viewers with a more diverse range of video content. The results of our study demonstrate the potential of using GPT-3 for narrative extraction on YouTube and provide a new approach for exploring video content on the platform.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379544751Subjects--Topical Terms:

554358
Information science.
Subjects--Index Terms:

GPT 3Index Terms--Genre/Form:

542853
Electronic books.

Extracting Narratives Using GPT-3 From Youtube Videos and Its Visualization.
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500 $a Source: Masters Abstracts International, Volume: 84-11.
500 $a Advisor: Agarwal, Nitin.
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504 $a Includes bibliographical references
520 $a YouTube, the leading video-sharing social media platform, is home to a vast number of videos, with over one billion hours of content consumed daily. However, with 500 hours of fresh content added to the platform every day, ensuring the quality of the content has become a daunting task, making it challenging to prevent the spread of low-quality information. The manual review of malicious content by moderators is time-consuming and highlights the need for faster analysis and processing tools.This paper proposes a novel approach that utilizes Generative Pre-Trained Transfer-3 (GPT-3), with 175 billion parameters, to extract narratives from YouTube videos. The proposed model achieved a semantic similarity of 70% with state-of-the-art frameworks designed for narrative extraction. Furthermore, the study addresses the issue of content exploration on YouTube, as viewers tend to be recommended similar videos by the platform's algorithm. To address this issue, we propose the integration of the extracted narratives into a web-based visualization tool that enables users to explore different variations of the same or combined keywords. The proposed tool aims to enhance the quality of the content and provide viewers with a more diverse range of video content. The results of our study demonstrate the potential of using GPT-3 for narrative extraction on YouTube and provide a new approach for exploring video content on the platform.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Information science. $3 554358
650 4 $a Web studies. $3 2122754
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653 $a Narrative visualization
653 $a YouTube
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