Blacklist Creation for Detecting Fake Accounts on Twitter

DOI

10.2991/ijndc.2018.7.1.6How to use a DOI?

Keywords

Topic extraction approach; keyword extraction approach; DECORATE

Abstract

Social networking sites such as Twitter, Facebook, Weibo, etc. are extremely mainstream today. Also, the greater part of the malicious users utilize these sites to persuade legitimate users for different purposes, for example, to promote their products item, to enter their spam links, to stigmatize other persons, etc. An ever increasing number of users utilize these social networking sites and fake accounts on these destinations are turned into a major issue. In this paper, fake accounts are detected using blacklist instead of traditional spam words list. Blacklist is created using topic modeling approach and keyword extraction approach. We evaluate our blacklist based approach on 1KS–10KN dataset and Social Honeypot dataset and compared the accuracy with the traditional spam words list based approach. Diverse ensemble creation by oppositional relabeling of artificial training examples, a meta-learner classifier is applied for classifying fake accounts on Twitter from legitimate accounts. Our approach achieves 95.4% accuracy and true positive rate is 0.95.

Copyright

© 2018 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Myo Myo Swe
AU  - Nyein Nyein Myo
PY  - 2018
DA  - 2018/12/31
TI  - Blacklist Creation for Detecting Fake Accounts on Twitter
JO  - International Journal of Networked and Distributed Computing
SP  - 43
EP  - 50
VL  - 7
IS  - 1
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.2018.7.1.6
DO  - 10.2991/ijndc.2018.7.1.6
ID  - Swe2018
ER  -