The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Cyberbullying Detection using Recursive Neural Network through Offline Repository
Abstract
The objective of this paper is to predict the user behaviour based on his posts on social networking sites specifically Twitter. The data sets are captured through secured API's exposed by these social network sites and stored in data lakes or NoSQL databases. Tensorflow API's have been used to do predictive analysis of this stored data through recursive networks. This paper is to demonstrate the identification of specific text from the data which is available in various forms - structured and unstructured and coming from various online sources in real time, posted by users worldwide. The online sources referred to in this paper are social networking sites, twitter etc. where multiple users collaborate with each other and post contents. To demonstrate the approach, the Information is captured from these sites through API exposed by these vendors which is captured in NoSQL databases and then NLP is applied to break the text which is then applied against text corpus to identify analogous data. From programming perspective data strictures are used to store the data at run-time. Specific WordNet API is being leveraged for their capabilities to find synonyms.
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“We find that several studies used TensorFlow [14,129,182,183] in their experiments, because the framework is flexible, easy to use and suits natural language processing tasks.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [129]
Citations
Cites
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Modeling the Detection of Textual Cyberbullying
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Cited by
Sorted by citations
A Review on Deep-Learning-Based Cyberbullying Detection
Hasan, M. T., Hossain, M. A. E., Mukta, M. S. H., Akter, A., Ahmed, M., & Islam, S. (2023) · Future Internet · doi:10.3390/fi15050179 · Cited by 103
“Wide range of models including CNN, RNN, GAN, Transformer, etc. [155] Chats and Tweets [14], Bangla Text [18], Offline Content [129], Social Media text analysis [112], Comments and Toxicity [156], Multilingual Tweets and Hate speech [157], Wikipedia talk page [158], Post of Social Network platform…”
BackgroundMethod
Metadata
- Authors
- Chandra, N., Khatri, S. K., & Som, S.
- Affiliations
- Amity University (IN)
- Venue
- 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)
- Publisher
- IEEE
- Published
- 2018-08
- Volume
- 25
- Pages
- 748-754
- OpenAlex
- W2954780012
- Cited by
- 9
- References
- 18
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 1 · 2020: 2 · 2019: 2
- FWCI
- 0.51
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Keywords
- cyberbullying detection, recursive neural networks, Twitter, social networking sites, user behavior prediction, text classification, NoSQL databases, data lakes, natural language processing
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