Python
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. It outperformed the considered existing approaches in detecting CB on Twitter platform.
Existing System:
Cyberbullying detection within the Twitter platform has largely been pursued through tweet classification and to a certain extent with topic modeling approaches. Text classification based on (DL) models are commonly used for classifying tweets into bullying and non-bullying tweets. Supervised classifiers have low performance in case the class labels are unchangeable and are not relevant to the new events. Also, it may be suitable only for a pre-determined collection of events, but it cannot successfully handle tweets that change on. Topic modeling approaches have long been utilized as the medium to extract the vital topics from a set of data to form the patterns or classes in the complete dataset. Although the concept is similar, the general unsupervised topic models cannot be efficient for short texts, and hence specialized unsupervised short text topic models were employed. These models effectively identify the trending topics from tweets and extract them for further processing. These models help in leveraging the processing to extract meaningful topics. However, these unsupervised models require extensive training to obtain sufficient prior knowledge, which is not adequate in all cases.Considering these limitations, an efficient tweet classification approach must be developed to bridge the gap between the classifier and the topic model so that the adaptability is significantly proficient.
Problem Definition:
DL models have low performance in case the class labels are unchangeable and are not relevant to the new events .Also, it may be suitable only for a pre-determined collection of events, but it cannot successfully handle tweets that change on.
Proposed System:
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 cyberbullying 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 cyberbullying keywords for evaluating the performance methods; and The efficiency in recognizing and classifying cyberbullying tweets is assessed using Twitter datasets. The thorough experimental results reveal that ML model.
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