**4. Summary**

The new system is achieved by two commitments. First, a Novel Statistical Application, which is established on the new Bully-LDA with the weighted B-TFIDF strategy on bullying like attributes. It also efficiently and effectively finds latent bullying features to cultivate the accomplishment of the classifier and also to reduce the feature sparsity. Secondly, a Graph Model lends a hand to pinpoint the attackers and causalities in social networks. Such a system would encompass the following function: Tweets Crawling, Tweet Preprocessing and Tokenization, Feature extraction and Frequency extraction, Text Representation Model, Text Classification, Category of Texts, Performance Evaluation, and Results.

The Twitter corpus consists of text communications by way of metadata such user ID, dispatching time, etc. Tweets Crawling is performed using many classes and techniques in order to get the information of the users' connected data and the details of the Tweets' which is done using Twitter's Application programming interface called "Twitter4j-core-4.02.jar." Tweets are shown in entirely colloquial manner, with more amount noise and variation in linguistics. For example, tweets contain a hefty quantity of novel words, interjections, repetitions, short words such as acronyms, words with missing letters, words with phonetic spelling like *Gud* for *Good,* etc. and also missing blank spaces between the words, such as *whatareyoudoing,* which increases the tweet length. All these things impose a huge burden in the analysis of the text. Text preprocessing module contains word segmentation, word processing, and subsequent analytical steps include like converting uppercase letters to lower case, stemming, eradicating stop words, superfluous characters and hyperlinks.

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Content-driven detection of

The proposed framework utilizing Bully-Latent Dirichlet Allocation through Support Vector Machine has been examined with Twitter messages. This system is based on a novel concept of applying text mining techniques to tweets for detecting Bullying messages and also to identify Predators. The weighted B-TFIDF function is used to enhance the execution of classification, in which bullying-like features are measured. The overall results using Bully-LDA + SVM and weighted B-TFIDF outperformed other models. This model has numerous benefits adding more accuracy, superior noise diminution, faster speed and greater automation. The results obtained were analyzed properly using different metrics. A range of performance measures for instance accuracy, recall and F1 measures were calculated. The analysis of results plainly displays that the system yields effective results in identifying bullying messages in a successful manner.

In this research, a methodology for cyber bullying recognition of the most operative predators and casualties are done powerfully and fruitfully. This chapter presents a framework for detecting cyber bullying in Twitter using Bully-Latent Dirichlet Allocation with support vector machine. The preprocessing procedures have pertained to tweets. First Bully-LDA, a statistical topic modeling is used on a massive Twitter Corpus, with the help of weighted B-TFIDF scheme to detect offensive words in tweets. Next, a graph representation is utilized to recognize the predators and casualties in Twitter.
