Automatic Detection of Cyberbullying

Automatic detection of Cyberbullying
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Overview
Web 2.0 is comprised of a substantial impact on the relationship and communication within the
current time. Although many internet use teenagers are harmless, and also the benefits of the
communication in digital form are evident, the anonymity and freedom have experienced the
making online young people highly vulnerable by making using of cyberbullying that has been
one of the biggest threats. The cyber bullying manifested as digital technology has become the
tool of primary communication. Also on the positive aspects, the blogs on social media, sites of
social networking, and platforms of instant messaging create this possible for communicating
with anyone anywhere anytime. So, they are within the place where people might engage in
social interaction, offering the possibility of the establishment of newer relationships and
maintaining the existing links. On negative aspects, the social media enhances the children risk
which has been confronted with the situations threats comprised with sexually or grooming
transgressive behavior, depression signals and thoughts (suicidal), and also cyberbullying.
This report proposes the method of machine learning using Python for the detection of
cyberbullying by creating the usage of linear classifier exploitation of the numerous feature sets.
This has been an initial approach for the annotation of grained finely categories of text associated
with cyberbullying and also signals detection of the events of cyberbullying. This report has been
focused on the experimenting and analysis of extraction of features and detection of the cyber
bullying within social sites by making use of the NLP (Natural Language Processing) tools and
numerous algorithms of machine learning using Python.

Objectives
The main objective of this report is the detection of the model of cyberbullying would help
within enhancing the manual type of monitoring for cyberbullying through social networks. This
project will help in fetching the tweets from social media and then it will preprocess the images
and tweets and apply the model generated that helps in the detection of cyberbullying (Pawar, et
al., 2019).
The aims of the development of such a system and the management of events are as shown
below:
 Collecting the sets of data of the bullying words and then preprocessing it and applying
the NLP (Natural language Processing) and then the algorithm of machine learning using
python generated numerous algorithms of machine learning model.
 Fetches the tweets from the social network accounts and then preprocess them.
 Applies the generated model to the tweets that have been fetched and carries the final
output that is cyberbullying or not.
Social networks offer us a huge platform for communication and also enhance the vulnerability
of the younger generation in threatening situations online. Cyberbullying on social networks has
been a global phenomenon because of its huge quantity of users (active). Per the trend that shows
that bullying online through social networks has been enhancing frequently each day. So, the
successful prevention has been dependent on the detection of messages that are potentially
harmful and the overloading of data on the web needs an intelligent system within the identity of
the potential type of risk in an automated manner.

How the Objectives will be achieved
This project has been developed by making use of the Python and technology of the web.
Initially, we would search and then find the sets of data and download them for training the
model generated. After carrying out the downloading it needs to be preprocessed of the data and
then this might be transferred to the Tf-Idf (Technique for quantifying the words in documents).
Then, by making use of the naïve Bayes theorem, Support vector machine (SVM), and also the
algorithm of DNN we will train the sets of data and generate the model in a separate manner.
After that, we need to develop the application on basis of the web by making use of the
framework i.e., FLASK. We would fetch the tweets in real-time from a social network like
Twitter and check the images or text that has been cyberbullying or not. These entire things will
be carried out by using Python as the backend, the database users will be MySQL and for the
frontend JavaScript, CSS, and HTML will be used (Salawu, et al., 2017).