DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform
Product Description
Aim:
Cyberbullying (CB) has become increasingly prevalent in social media platforms. With the popularity and widespread use of social media by individuals of all ages, it is vital to make social media platforms safer from cyberbullying. This esents a hybrid Machine learning model, to detect CB on Twitter social media network.
Abstract:
Social media networks such as Facebook, Twitter, Flickr, and Instagram have become the preferred online platforms for interaction and socialization among people of all ages. While these platforms enable people to communicate and interact in previously unthinkable ways, they have also led to malevolent activities such as cyber-bullying. Cyberbullying is a type of psychological abuse with a significant impact on society. Cyber-bullying events have been increasing mostly among young people spending most of their time navigating between different social media platforms. In India, for example, 14 percent of all harassment occurs on Facebook and Twitter, with 37 percent of these incidents involving youngsters. Moreover, cyberbullying might lead to serious mental issues and adverse mental health effects. Most suicides are due to the anxiety, depression, stress, and social and emotional difficulties from cyber-bullying events. This motivates the need for an approach to identify cyberbullying in social media messages (e.g., posts, tweets, and comments)
Synopsis:
The proposed ML model combines and evaluated thoroughly utilizing a dataset of 10000 tweets and compared its performance to those of state-of-the-art algorithms such as Logistic regression, SVM, Multinomial Naive Bayes (MNB), Random Forests (RF). It outperformed the considered existing approaches in detecting CB on Twitter platform.
In this model, we propose a Machine learning-based approach, which automatically detects bullying from tweets. Machine learning Models outperformed the considered existing approaches in detecting cyber bullying on the Twitter platform in all scenarios and with various evaluation metrics.
Advantage:
Propose ML Algorithms by classification of tweets; A new Twitter dataset is collected based on cyber bullying keywords for evaluating the performance methods; and The efficiency in recognizing and classifying cyber bullying tweets is assessed using Twitter datasets. The thorough experimental results reveal that ML model outperforms other competing models in terms of recall, precision, accuracy, F1 score, and specificity.
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The Delivery time for Hardware Mini projects is 7-8 working days.