BulliShield: A Smart Cyberbullying Detection and Reporting System

被引:0
作者
Tahmid, Farhan Ishrak [1 ]
Akbar, Farhana [1 ]
Rahman, Ahsanur [1 ]
机构
[1] North South Univ, Elect & Comp Engn, Dhaka, Bangladesh
来源
PROCEEDINGS 2024 SEVENTH INTERNATIONAL WOMEN IN DATA SCIENCE CONFERENCE AT PRINCE SULTAN UNIVERSITY, WIDS-PSU 2024 | 2024年
关键词
cyberbullying; machine learning; deep learning; AI; artificial intelligence; hate speech detection;
D O I
10.1109/WiDS-PSU61003.2024.00048
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
BulliShield is an intelligent and portable application that combines detection, reporting, and AI-based counseling to offer an integrated approach to solve the widespread problem of cyberbullying. Within the system's user-friendly architecture, registered users can file complaints about possible instances of cyberbullying. Since institutions typically require the victim to submit photo evidence showing a snapshot of the whole electronic conversation with the bully, BulliShield allows victims to submit photo evidence of conversations with the bully. Our app extracts texts from those photos using OCR, identifies the language (currently, it can only detect hate speech in Bangla and English texts), and then employs appropriate machine/deep learning techniques to identify potentially offensive content in those texts, thereby helping the appropriate authority (for example, a proctor in the case of a university). BulliShield allows authorities to review complaints, call both parties for meetings, add notes, save their judgments to resolve the case, and so on - thereby acting as a fully functional system for handling cyberbullying cases. BulliShield ensures accountability and transparency throughout the resolution process by keeping the plaintiff informed about the current status of the complaint. BulliShield also offers an AI-based counseling component, giving the victims easy and free access to a technology that can help them develop resilience and emotional well-being.
引用
收藏
页码:198 / 203
页数:6
相关论文
共 12 条
[1]   Improving cyberbullying detection using Twitter users' psychological features and machine learning [J].
Balakrishnan, Vimala ;
Khan, Shahzaib ;
Arabnia, Hamid R. .
COMPUTERS & SECURITY, 2020, 90
[2]  
Fan Mingyue, 2016, P 2016 CHI C HUM FAC, P1187, DOI [10.1145/2851581.2892398, DOI 10.1145/2851581.2892398]
[3]   Cyberbullying detection in social media text based on character-level convolutional neural network with shortcuts [J].
Lu, Nijia ;
Wu, Guohua ;
Zhang, Zhen ;
Zheng, Yitao ;
Ren, Yizhi ;
Choo, Kim-Kwang Raymond .
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2020, 32 (23)
[4]   Adolescent victims of cyberbullying in Bangladesh-prevalence and relationship with psychiatric disorders [J].
Mallik, Chiro Islam ;
Radwan, Rifat Binte .
ASIAN JOURNAL OF PSYCHIATRY, 2020, 48
[5]   Mobile Edutainment Learning Approach: #StopBully [J].
Neo, Han-Foon ;
Teo, Chuan-Chin ;
Boon, Jackson Lew Han .
PROCEEDINGS OF 2018 2ND INTERNATIONAL CONFERENCE ON DIGITAL TECHNOLOGY IN EDUCATION (ICDTE 2018), 2018, :6-10
[6]  
Rani M. U., 2023, Journal of Engineering Sciences, V14
[7]  
Salawu S., 2022, INT J BULLYING PREV, V4, P66, DOI [10.1007/s42380-021-00115-5, DOI 10.1007/S42380-021-00115-5]
[8]  
Salawu S., 2020, P 28 INT C COMP LING, P70
[9]   BullyBlocker: toward an interdisciplinary approach to identify cyberbullying [J].
Silva Y.N. ;
Hall D.L. ;
Rich C. .
Social Network Analysis and Mining, 2018, 8 (01)
[10]  
Tan S. Y., 2021, Turkish Journal of Computer and Mathematics Education (TURCOMAT), V12, P1805