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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Cyberbullying In Social Networking Sites Using Deep Learning Model |
| Authors Name: | Sanjay GJ , Sudhir kumaar S J , Nandhakumar G , Sandeep Kumar Varma M |
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| Author Reg. ID: |
IJRTI_186743
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| Published Paper Id: | IJRTI2305103 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | |
| Abstract: | Cyberbullying is bullying that takes place over digital devices like cell phones, computers, and tablets. Cyberbullying can occur through SMS, Text, and apps, or online in social media, forums, or gaming where people can view, participate in, or share content. Cyberbullying includes sending, posting, or sharing negative, harmful, false, or mean content about someone else. It can include sharing personal or private information about someone else causing embarrassment or humiliation. The content an individual share online – both their personal content as well as any negative, mean, or hurtful content – creates a kind of permanent public record of their views, activities, and behaviour. To avoidor detecting cyberbullying attacks, many existing approaches in the literature incorporate Machine Learning and Natural Language Processing text classification models without considering the sentence semantics. The main goal of this project is to overcome that issue. This project proposed a model LSTM - CNN architecture for detecting cyberbullying attacks and it used word2vec to train the custom of word embeddings. This model is used to classify tweets or comments as bullying or non-bullying based on the toxicity score.LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series. A convolutional neural network (CNN) is a type of artificial neural network and it has a convolutional layer to extract information by a larger piece of text and by using this model LSTM- CNN achieve a higher accuracy in analysis, classification and detecting the cyberbullying attacks on posts and comments. |
| Keywords: | Cyberbullying, Social networking sites, Deep learning, Online harassment, Cyberbullying detection, Machine learning, Social media platforms, Cyberbullying prevention, Online safety, Natural language processing, Text classification, Sentiment analysis, Hate speech detection, User profiling, Cyberbullying intervention, Social network analysis, Online bullying behavior, Online reputation management, Data mining, Cyberbullying awareness. |
| Cite Article: | "Cyberbullying In Social Networking Sites Using Deep Learning Model", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 5, page no.654 - 658, May-2023, Available :http://www.ijrti.org/papers/IJRTI2305103.pdf |
| Downloads: | 000205162 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2305103
Registration ID:186743
Published In: Volume 8 Issue 5, May-2023
DOI (Digital Object Identifier):
Page No: 654 - 658 Country: Salem, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2305103 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2305103 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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