Zobrazeno 1 - 10
of 296
pro vyhledávání: '"cyberbullying detection"'
Autor:
Fakhra Razi, Naveed Ejaz
Publikováno v:
IEEE Access, Vol 12, Pp 105201-105210 (2024)
Automatic cyberbullying detection in social media is increasingly vital due to the integral role of social networks in people’s lives and the severe impact of cyberbullying. Cyberbullying involves intentional, repetitive, aggressive behaviour to ha
Externí odkaz:
https://doaj.org/article/4963fd1179144c08bf7d7884591acafe
Autor:
Azhi Faraj, Semih Utku
Publikováno v:
UHD Journal of Science and Technology, Vol 8, Iss 1, Pp 55-63 (2024)
Cyberbullying has emerged as a pervasive concern in modern society, particularly within social media platforms. This phenomenon encompasses employing digital communication to instill fear, threaten, harass, or harm individuals. Given the prevalence o
Externí odkaz:
https://doaj.org/article/1d28cc4172184fa4ba3224004b2f98d8
Publikováno v:
IEEE Access, Vol 12, Pp 123339-123351 (2024)
In today’s digital era, the escalating phenomenon of cyberbullying is a pervasive and growing concern. With the increasing prevalence of social media platforms, such as Twitter, online abusive behavior has become a significant issue that often lead
Externí odkaz:
https://doaj.org/article/f4d53ec3d53a43b4aedab5f3f03d51d9
Publikováno v:
IEEE Access, Vol 12, Pp 105588-105604 (2024)
Accurate detection of cyberbullying on Social Networking Sites (SNSs) is crucial for online safety, especially for individuals impacted by it. Cyberbullying language is often implicit, necessitating a comprehensive analysis of conversational context
Externí odkaz:
https://doaj.org/article/ba628c70f16d441baf257f6151535561
Autor:
Eman-Yaser Daraghmi, Sajida Qadan, Yousef-Awwad Daraghmi, Rami Yousuf, Omar Cheikhrouhou, Mohammed Baz
Publikováno v:
IEEE Access, Vol 12, Pp 103504-103519 (2024)
Several research on cyberbullying detection have employed different deep learning and machine learning methodologies to achieve promising outcomes. Nevertheless, most of them have primarily concentrated on using English data for both purposes: traini
Externí odkaz:
https://doaj.org/article/d8b97590aa0541cbb2806c55347c54b1
Autor:
Farah Adeeba, Muhammad Irfan Yousuf, Izza Anwer, Sardar Umair Tariq, Abdullah Ashfaq, Malik Naqeeb
Publikováno v:
PeerJ Computer Science, Vol 10, p e1963 (2024)
The prevalence of cyberbullying has reached an alarming rate, affecting approximately 54% of teenagers who experience various forms of cyberbullying, including offensive hate speech, threats, and racism. This research introduces a comprehensive datas
Externí odkaz:
https://doaj.org/article/b4231fb6a4e847189fbcd8447cc61604
Publikováno v:
Analytics, Vol 3, Iss 1, Pp 1-13 (2023)
With the development of the Internet, the issue of cyberbullying on social media has gained significant attention. Cyberbullying is often expressed in text. Methods of identifying such text via machine learning have been growing, most of which rely o
Externí odkaz:
https://doaj.org/article/6b97be0546ef44398242a05af1ab266d
Publikováno v:
Vietnam Journal of Computer Science, Vol 10, Iss 02, Pp 135-158 (2023)
Cyberbullying has become a serious problem with the spread of personal computers, smartphones and SNS. In this paper, for automated cyberbullying detection on Twitter, we construct a Japanese bullying expression dictionary, which registers bullying w
Externí odkaz:
https://doaj.org/article/586c88ac73ea47eebaaa34609de1e024
Publikováno v:
Teknika, Vol 12, Iss 3 (2023)
Pada era digital seperti sekarang cyberbullying kerapkali terjadi di berbagai belahan dunia termasuk di Indonesia, hal ini dapat terjadi pada siapa saja dan dimana saja terutama media sosial seperti YouTube melalui fitur komentar semua pengguna yang
Externí odkaz:
https://doaj.org/article/04d47ad4fa4942f79aaf366ede8f2fbd
Publikováno v:
IEEE Access, Vol 11, Pp 124524-124541 (2023)
Cyberbullying has emerged as a pervasive issue in the digital age, necessitating advanced techniques for effective detection and mitigation. This research explores the integration of word embeddings, emotional features, and federated learning to addr
Externí odkaz:
https://doaj.org/article/b0e7a84eae49427a8baa7d9bdd52aa1d