Multimodal cyberbullying detection using capsule network with dynamic routing and deep convolutional neural network
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Theano is heavily used in cyberbullying-related tasks.
“Theano is another heavily used framework in cyberbullying-related tasks [137,170,171] due to its quick processing speed.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [171]
Keras is a popular framework for cyberbullying detection.
“However, Keras is also a popular framework [20,25,171,184] for cyberbullying detection since the framework is built to provide a simple interface for quick prototyping by building active neural networks that can work upon TensorFlow.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [171]
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References 48 · Cited by 97
- Authors
- Kumar, A., & Sachdeva, N.
- Affiliations
- Delhi Technological University (IN)
- Corresponding author
- Kumar, Akshi
- ORCID
- Kumar, Akshi
- Venue
- Multimedia Systems
- Published
- 2021-02-02
- Volume
- 28
- Issue
- 6
- Pages
- 2043-2052
- ISSN
- 0942-4962 (ISSN-L)
- 1432-1882
- OpenAlex
- W3128060799
- Cited by
- 97
- References
- 48
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 19 · 2023: 27 · 2022: 18 · 2021: 12
- FWCI
- 6.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Multimedia Systems (publishedVersion)
- e-space (Manchester Metropolitan University) (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- cyberbullying detection, capsule networks, dynamic routing, convolutional neural networks, multimodal learning, text-image fusion, social media, infographics, YouTube comments, Instagram posts, tweets
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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