Evidence map›Paper›PMID 40664816›Full record

ArticleScientific reports2025

Utilizing CBNet to effectively address and combat cyberbullying among university students on social media platforms.

Irshad Ahmed Abbasi, Muhammad Shoaib, Mohammed Alshehri, Mohammed Aldawsari

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Irshad Ahmed AbbasiDepartment of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha, 67714, Saudi Arabia. aabasy@ub.edu.sa.
Muhammad ShoaibDepartment of Computer Science and Information Technology, Sarhad University of Science and Information Technology, Peshawar, 25000, Pakistan.
Mohammed AlshehriDepartment of Computer Science, Applied College, King Khalid University, Abha, 61421, Saudi Arabia.
Mohammed AldawsariDepartment of Computer Engineering and Information, College of Engineering, Wadi Ad Dwaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, 16273, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cyberbullying can profoundly impact individuals' mental health, leading to increased feelings of anxiety, depression, and social isolation. Psychological research suggests that cyberbullying victims may experience long-term psychological consequences, including diminished self-esteem and academic performance. The widespread use of social media platforms among university students has raised major concerns over cyberbullying, which can have detrimental effects on student mental well-being and academic performance. The purpose of this study is to develop Cyberbullying Network (CBNet), a novel Convolutional Neural Network (CNN)-based model, and a comprehensive dataset to enhance cyberbullying detection accuracy and real-world applicability in student social media interactions. We designed CBNet, a CNN-based model for detecting cyberbullying among student social media groups. We developed a comprehensive dataset collected from several social media platforms popular among university students. Our results demonstrate that CBNet notably outperforms both the traditional machine learning approaches and the Recurrent Neural Network (RNN)-based model and presents an outstanding value of precision, recall, and F1-score overall, with an Area Under the ROC Curve significantly higher than 0.99. Combined with the fact that the issue of cyberbullying always remains relevant, these results suggest the high feasibility of our suggested approach to the detection of incidents. Given our results, CBNet could be used as a preventative tool for educators, administrators, and community managers to combat cyberbullying behavior and make the online community safer and more welcoming for students. This work suggests the high importance of advanced machine learning approaches to real-world social problems and contributes to the creation of greater digital well-being in university students' communities. By employing CBNet, institutions can take proactive measures to mitigate the harmful effects of cyberbullying and cultivate a positive online culture conducive to student success and flourishing.

Indexed as

CyberbullyingNeural Networks, ComputerSocial MediaStudentsFemaleHumansMaleUniversitiesYoung AdultCyberbullyingMachine learningOnline communitiesSocial media platformsUniversity students

Identifiers

PMID40664816
PMCPMC12264107

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.