國立虎尾科技大學 |

Detection of Cyberbullying in GIF /Stickers Using AI.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Detection of Cyberbullying in GIF /Stickers Using AI./
Author:	Dave, Pal.
Description:	1 online resource (45 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-07.
Contained By:	Masters Abstracts International85-07.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798381427967

Detection of Cyberbullying in GIF /Stickers Using AI.
Dave, Pal.

Detection of Cyberbullying in GIF /Stickers Using AI. - 1 online resource (45 pages)

Source: Masters Abstracts International, Volume: 85-07.

Thesis (M.S.)--North Carolina Agricultural and Technical State University, 2023.

Includes bibliographical references

Cyberbullying is a well-known social issue, and it is escalating day by day. Bullying does not just happen in person; it has taken over all social media. Cyberbullying can be done intentionally or unintentionally by the social media users themselves. A user can use social media as a platform to bully a victim. It can be unintentional because sometimes a user might not know what they are doing and how it can impact others. Mostly out of curiosity or without being concerned, users upload, share and write their views on social media which can affect a community or a person mentally and emotionally. Due to the vigorous development of the internet, social media provides many different ways for the user to express their opinions and exchange information. Cyberbullying occurs on social media using text messages, comments, sharing images and GIFs or stickers, and audio and video. Much research has been done to detect cyberbullying on textual data; some are available for images. Very few studies are available to detect cyberbullying on GIFs/stickers.In this thesis, we collected a GIF dataset from Twitter and Applied a deep learning model to detect cyberbullying from the dataset. Firstly, we extracted hashtags related to cyberbullying using Twitter. We used these hashtags to download GIF files using publicly available API GIPHY. We collected over 4100 GIFs including cyberbullying and non-cyberbullying. Second, we applied a deep learning pre-trained model VGG16 for the detection of cyberbullying. The deep learning model achieved an accuracy of 96.62%. Our work provides the GIF dataset for researchers working in this area.

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

Mode of access: World Wide Web

ISBN: 9798381427967Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

CyberbullyingIndex Terms--Genre/Form:

554714
Electronic books.

Detection of Cyberbullying in GIF /Stickers Using AI.
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