Open AccessA Machine Learning Method for Spam Detection in Twitter using Naive Bayes and ERF Algorithms
Author(s) -
M. Arunkrishna,
B. Mukunthan
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f4729.049620
Subject(s) - popularity , social media , computer science , naive bayes classifier , phishing , hacker , malware , internet privacy , machine learning , random forest , social media optimization , world wide web , artificial intelligence , computer security , the internet , support vector machine , psychology , social psychology
In this era of machinery driven, online social media is a vast growing fact. The main social media is Instagram, Facebook and twitter. These are the media which are connecting the global as fast as other sources. It will be increase as tremendous way in future. These online social media users makes the information independently and also they can gobble the information. There are so many domains accepts the vital role of analyzing the social media. This may improves the throughput and also attain the back-and-forth competition. Now a day the people are spending their most of the time in the online social media. The vast increase in the popularity in the social media also makes the hackers to spam, thus causes the conceivable losses. The Cyber criminals are usually hack by produce the external phishing sites or the malware downloads. This became the major issues in the safety consideration of online social network and this makes the user experience as a damaged one. To combat with the issue of spams, there has been a lot of methods available, Yet, there is not a perfect effective solution for detect the Twitter spams with the exactness. In this paper , the collected tweets are classified with the help of NB and Enhanced Random Forest classifiers. The prediction is then assessed on many validation measures such as accuracy,precision and F1 score.