Course Content

ABSTRACT

 

The popularity of Twitter attracts more and more spammers. Spammers send unwanted tweets to Twitter users to promote websites or services, which are harmful to normal users. In order to stop spammers, researchers have proposed a number of mechanisms. The focus of recent works is on the application of machine learning techniques into Twitter spam detection. Many previous works have focused on detection of malicious user accounts. Detecting spams or spammers on twitter has become a recent area of research in social network. However, we present a method based on two new aspects: the identification of spam-tweets without knowing previous background of the user; and the other based on analysis of language for detecting spam on twitter in such topics that are in trending at that time.

So we proposed a system with the help of machine learning techniques, NLP techniques like count vectorizer, TF-IDF Transformer and Machine learning algorithms like Logistic Regression ,Naïve Bayes and XGB Classifier, Random Forest classifier, Decision Tree Classifier,  KNN and SVC  to predict the Tweet is Spam or Real based on Tweet entered by the user in the front end.

 

INTRODUCTION

Social media plays vital role among the user communities for social gathering, entertainment, communication, sharing knowledge so on. Twitter is one such network to connect millions of users to share information. Nowadays, there are humpteen numbers of users using social media for social engagements. Due to the fact that wide publicity of individuals and products get viral in social media, everyone wish to use social media as a platform to promote their product. Furthermore, large number of people relies on social media contents to take decisions. Twitter is one of the social media platforms to post the media contents by the user. Spammers are illegal users intrude the twitter account and send the duplicate messages to promote advertisement, phishing, scam and personal blogs etc.The prevalence of web has expanded the utilization of web based business exchanges. Numerous online business permits clients to survey item in light of its experience. So it can be useful to different clients to settle on choices for purchasing items. Surveys are exceptionally basic in web based business site, reviews can be valuable to costumers to take choice in buy of item and likewise helpful for association to make quality change. It can be extremely valuable to make business focused. In any case, numerous clients or association present spam reviews on advance or slander brand or particular item. Spam reviews are Very basic these days in internet business sites. Numerous business associations enlist individuals to post fake reviews sake of them. So detection of spam reviews becomes important nowadays. We concentrate on utilizing Twitter, the most well known microblogging stage, for the undertaking of notion examination. We demonstrate to consequently gather a corpus for feeling investigation and assessment mining purposes

So we proposed a system with the help of machine learning techniques, NLP techniques like count vectorizer, TF-IDF Transformer and Machine learning algorithms like Logistic Regression ,Naïve Bayes and XGB Classifier, Random Forest classifier, Decision Tree Classifier,  KNN and SVC  to predict the Tweet is Spam or Real based on Tweet entered by the user in the front end.

 Objective

The main aim of this project is to predict whether a tweet is spam or real using machine learning techniques, NLP techniques and machine learning algorithms like Logistic Regression, Naïve Bayes and XGB Classifier, Random Forest classifier, Decision Tree Classifier, KNN and SVC with good accuracy based on tweet entered by the user in the front end.

Problem Statement

Twitter is one of the most popular social networks that, users can send short textual messages namely tweet. Researches have shown that this network is subject to spammer’s invasion more than other social networks and more than six percent of its tweets are spam. So diagnose of the spam tweets is very important.

 

 Proposed System:

We proposed a system with the help of machine learning techniques, NLP techniques like count vectorizer, TF-IDF Transformer and Machine learning algorithms like Logistic Regression ,Naïve Bayes and XGB Classifier, Random Forest classifier, Decision Tree Classifier,  KNN and SVC  to predict the Tweet is Spam or Real based on Tweet entered by the user in the front end.