Project Title: DEARNN – A Hybrid Deep Learning Approach for Cyberbullying Detection on the Twitter Social Media Platform
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Project Overview
Cyberbullying is a pervasive issue on social media platforms, particularly on Twitter, where character limits and rapid interactions can lead to abusive behavior. The DEARNN project aims to develop a robust detection system utilizing a hybrid deep learning approach to identify and mitigate instances of cyberbullying in real-time. By leveraging the integration of various deep learning techniques, the project seeks to enhance the accuracy and efficiency of cyberbullying detection, ultimately contributing to a safer online environment.
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Objectives
– To design and implement a hybrid deep learning model that effectively detects cyberbullying content on Twitter.
– To explore various neural network architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to enhance feature extraction and temporal analysis of tweets.
– To create a comprehensive dataset of tweets labeled as bullying, non-bullying, and neutral, ensuring a diverse representation of language and emotions.
– To evaluate the performance of the DEARNN model against existing methods and promote its integration into moderation tools on social media platforms.
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Methodology
1. Data Collection:
– Utilize Twitter’s API to gather a substantial dataset of tweets that have a high likelihood of containing bullying content.
– Employ Natural Language Processing (NLP) techniques to clean, preprocess, and label the data. This includes tokenization, lemmatization, and removing irrelevant content such as URLs and special characters.
2. Model Architecture:
– Hybrid Deep Learning Model: The DEARNN model will integrate CNNs for feature extraction from text and RNNs, specifically Long Short-Term Memory (LSTM) networks, for capturing the context and sequential dependencies of tweets.
– Employ attention mechanisms to enhance the model’s capability to focus on significant words or phrases that indicate bullying behavior.
3. Training and Testing:
– Split the dataset into training, validation, and test sets to ensure a fair evaluation of the model.
– Utilize cross-validation techniques to fine-tune hyperparameters and improve model performance.
– Implement data augmentation methods to enhance training data diversity and robustness.
4. Evaluation Metrics:
– Assess model performance using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC curves to quantify the effectiveness of cyberbullying detection.
– Benchmark the DEARNN model against existing state-of-the-art techniques to demonstrate improvements in detection rates.
5. Implementation:
– Develop a user-friendly interface for moderators or end-users, enabling them to input tweets and receive immediate feedback regarding the likelihood of cyberbullying.
– Integrate the model into a real-time monitoring system for Twitter, allowing for proactive measurement and reporting of bullying incidents.
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Expected Outcomes
– A validated and efficient hybrid deep learning model (DEARNN) capable of detecting cyberbullying with high accuracy on the Twitter platform.
– Contribution of a labeled and diverse dataset that can be used by other researchers in the field to expand on cyberbullying detection methodologies.
– Development of a practical tool for social media platforms, assisting moderators in identifying and addressing cyberbullying effectively.
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Impact
The DEARNN project aims to significantly reduce the prevalence of cyberbullying incidents on Twitter through advanced detection methods. By creating a safer social media environment, this project can help promote positive online interactions and support mental well-being, especially among vulnerable populations such as adolescents. The integration of such a detection system can pave the way for enhanced moderation tools and policies, influencing the broader approach to digital communication in today’s society.
Conclusion
DEARNN represents a forward-thinking initiative aimed at combating cyberbullying through innovative technology. By harnessing the power of hybrid deep learning, the project aspires to create meaningful change in how social media platforms monitor and manage abusive behavior, ultimately fostering a healthier digital community.
