Efficient Spam Detection on Social Network
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 7
Abstract
With the growth of social networking sites for communicating, sharing, storing and managing significant information, it is attracting cybercriminals who misuse the Web to exploit vulnerabilities for their illicit benefits.spammers are the malicious users who contaminate the information presented by legitimate users and in turn pose a risk to the security and privacy of social networksTwitter is a social network designed as an information sharing service that allows users to exchange messages up to 140 characters. These messages are known as tweets. In our thesis we gather data from Twitter. This data will be used to analyze the features,test and train our data that will be used for supervised classification in order to detect real malicious profiles(spammers and non spammers).Based on dataset and feature collection, testing and training a supervised machine learning model is introduced for spammers identification. We use the KNN based classification(spammer detection) model and compared with other classification technique.we evaluate our data and optimize the system too.
Authors and Affiliations
Girisha Khurana, Mr Lalit Kumar
Keywords
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