A hybrid Data-Driven framework for Spam detection in Online Social Network-Reference-Cited by-同舟云学术

A hybrid Data-Driven framework for Spam detection in Online Social Network

Published:2023 Issue: Volume:218 Page:124-132
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Kumar Chanchal,Bharti Taran Singh,Prakash Shiv

Publisher

Elsevier BV

Subject

General Engineering

Reference24 articles.

1. Number of monthly active Twitter users worldwide from 1st quarter 2010 to 1st quarter 2019, https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/.

2. A Novel Stream Clustering Framework for Spam Detection in Twitter;Tajalizadeh;IEEE Transactions on Computational Social Systems,2019

3. Asymmetric self-learning for tackling Twitter Spam Drift;Chen,2015

4. “Twitterspamdetector” a spam detection framework for twitter;Kabakus;International Journal of Knowledge and Systems Science,2019

5. A deep learning model for Twitter spam detection;Alom;Online Social Networks and Media,2020

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A novel control chart scheme for online social network monitoring using multivariate nonparametric profile techniques;Applied Network Science;2024-07-03

2. ALBERT4Spam: A Novel Approach for Spam Detection on Social Networks;Bilişim Teknolojileri Dergisi;2024-04-30

3. Extreme Learning Machine for Spammer Detection and Fake User Identification from Twitter;2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT);2024-04-06

4. Detection of Cloned Attacks in Connecting Media using Bernoulli RBM_RF Classifier (BRRC);Multimedia Tools and Applications;2024-02-21

5. Dataset of Arabic spam and ham tweets;Data in Brief;2024-02