{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:18:43Z","timestamp":1767183523576,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In an era where online interactions increasingly shape social dynamics, the pervasive issue of cyberbullying poses a significant threat to the well-being of individuals, particularly among vulnerable groups. Despite extensive research on text-based cyberbullying detection, the rise of visual content on social media platforms necessitates new approaches to address cyberbullying using images. This domain has been largely overlooked. In this paper, we present a novel dataset specifically designed for the detection of visual cyberbullying, encompassing four distinct classes: abuse, curse, discourage, and threat. The initial prepared dataset (cyberbullying visual indicators dataset (CVID)) comprised 664 samples for training and validation, expanded through data augmentation techniques to ensure balanced and accurate results across all classes. We analyzed this dataset using several advanced deep learning models, including VGG16, VGG19, MobileNetV2, and Vision Transformer. The proposed model, based on DenseNet201, achieved the highest test accuracy of 99%, demonstrating its efficacy in identifying the visual cues associated with cyberbullying. To prove the proposed model\u2019s generalizability, the 5-fold stratified K-fold was also considered, and the model achieved an average test accuracy of 99%. This work introduces a dataset and highlights the potential of leveraging deep learning models to address the multifaceted challenges of detecting cyberbullying in visual content.<\/jats:p>","DOI":"10.3390\/info16080630","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T10:28:09Z","timestamp":1753352889000},"page":"630","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-Class Visual Cyberbullying Detection Using Deep Neural Networks and the CVID Dataset"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5583-1253","authenticated-orcid":false,"given":"Muhammad Asad","family":"Arshed","sequence":"first","affiliation":[{"name":"School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zunera","family":"Samreen","sequence":"additional","affiliation":[{"name":"Department of Physics, The Manar College, Lahore 54770, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arslan","family":"Ahmad","sequence":"additional","affiliation":[{"name":"School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laiba","family":"Amjad","sequence":"additional","affiliation":[{"name":"School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1853-8436","authenticated-orcid":false,"given":"Hasnain","family":"Muavia","sequence":"additional","affiliation":[{"name":"School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1284-234X","authenticated-orcid":false,"given":"Christine","family":"Dewi","sequence":"additional","affiliation":[{"name":"School of Information Technology, Deakin University, Campus 221 Burwood Hwy, Burwood, VIC 3125, Australia"},{"name":"Department of Information Technology, Satya Wacana Christian University, Salatiga 50715, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2488-1653","authenticated-orcid":false,"given":"Muhammad","family":"Kabir","sequence":"additional","affiliation":[{"name":"School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan"},{"name":"Department of Experimental Medical Science, Biomedical Center (BMC), Lund University, 22184 Lund, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/TEM.2021.3068310","article-title":"Technology Roadmapping Using Text Mining: A Foresight Study for the Retail Industry","volume":"69","author":"Ozcan","year":"2022","journal-title":"IEEE Trans. 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