MONITORING SUSPICIOUS COMMUNICATION ON SOCIAL MEDIA USING DATAMINING

Abstract

Social media has become very popular in this technology-driven era, and most people social media for business and many other purposes. The Internet has made this possible to connect with the globe. Where this technology is used tor a positive sense, people used it for negative. Some people use these platforms for a bully, threats, and even terrorism discussion. The purpose ofthe proposed system is to monitor and identify the suspicious communications on Twitter by identifying the suspicious words in chats of users. To get effective results, we used machine learning algorithms to train our model. Machine learning is a spectacularly powerful tool to make predictions or forecasting based on the bulk amount of data. Data in text format should be cleaned to reduce the noise within. We have done pre-processing in which the text present in the post or message will be filtered, cleaned, and extracted. use some The root words and unnecessary data cleanout, which increase processing time and reduce cost. Then, in the next phase, feature selection and extraction of the text normalization, tokenization, stemming. After the extraction of completed using case features present in the text, feature weighting is done using techniques like TF/IDF. have used four different algorithms for better accuracy. The For identification, we algorithm we applied is Multinomial Naive Bayes, Decision Tree, Support Vector Machine, and Adaboost. We have compared their result and then select the best one. the results captured through software called Jupiter notebook using python All language. All the details of our work are mentioned in this report.