Open AccessFake User Detection in Twitter using Random forest algorithm with Python
Author(s) -
Sai Poojitha Bommadevara*,
Jitendra Agarwal,
Sarath Babu
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.g5919.059720
Subject(s) - python (programming language) , computer science , random forest , world wide web , classifier (uml) , internet privacy , social media , resource consumption , computer security , artificial intelligence , ecology , biology , operating system
Millions of users are engaged with social networking sites around the world. Social sites like twitter, Facebook have a large impact on rare unwanted consequences caused in our regular life in user’s interactions. In order to disperse a large amount of inappropriate and harmful data protruding social networking sites are made as a target platform for the spammers. Twitter is main example that has become one of the important platforms for unreasonable amount of spam in all the tomes for fake users to tweet and promote websites or services that crates a major effect for legitimate users and also it disturbs resource consumption. By resulting the opening for unusual and harmful information there is an increase of fake identities that expands invalid data. Research on current online social networks (OSN) is quite natural for detection of fake users on twitter. In this paper using random forest classifier and ROC curve to detect fake users.