This paper is published in Volume-5, Issue-2, 2019
Area
Network Security
Author
Subash G.
Co-authors
S. Yuvalatha, V. Lenin Kumar, G. Manimegalai
Org/Univ
Sree Sakthi Engineering College, Karamadai, Tamil Nadu, India
Pub. Date
16 March, 2019
Paper ID
V5I2-1295
Publisher
Keywords
Spammer detection, Spammer detection in Twitter, Network-based Twitter spammer detection, Extended hybrid approach

Citationsacebook

IEEE
Subash G., S. Yuvalatha, V. Lenin Kumar, G. Manimegalai. Extended hybrid approach for detecting spammers on Twitter, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Subash G., S. Yuvalatha, V. Lenin Kumar, G. Manimegalai (2019). Extended hybrid approach for detecting spammers on Twitter. International Journal of Advance Research, Ideas and Innovations in Technology, 5(2) www.IJARIIT.com.

MLA
Subash G., S. Yuvalatha, V. Lenin Kumar, G. Manimegalai. "Extended hybrid approach for detecting spammers on Twitter." International Journal of Advance Research, Ideas and Innovations in Technology 5.2 (2019). www.IJARIIT.com.

Abstract

One of the biggest social networks is Twitter for providing message posting and use direct message services via “tweets”.366 million currently active users on Twitter, Is the second largest space to share common news or message post. These features are also used by spammers on Twitter, spammers are not new on Twitter. spammers must be detected in improving the quality of Twitter message services. In this spammer detected by using metadata, content, interaction, and community-based features methods. Tweet meta information extracted and analyzed based on user-id, tweets,tweet-time and tweet-type. Content Features are extracted based on user posting content with URL, Mention-tags and hash-tags. Interaction and community-based features are analyzed by following and follower information. The proposed approach to introducing network-based features for spammer ratio detection by using unique IP address based user classification. Spammers can be detected by analyzing their tweets based on the extended hybrid approach by using the random forest, decision tree, and Bayesian network on the Twitter dataset that has benign users and spammers.