Authors :
Muhammad HashimHameed; Usman Rasheed; Akmal Rehan
Volume/Issue :
Volume 7 - 2022, Issue 8 - August
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3BcXEGV
DOI :
https://doi.org/10.5281/zenodo.7047236
Abstract :
In recent time, social media have been
affected many undesirable threats. Social media
provided us an open platform to connect and share our
life events with others. Social media also attracted the
attentions of the spammers. Spam in social media
relates to undesirable, malicious and spontaneous
content, shown in different ways including malicious
links, massages, fake friends and microblogs, etc. With
the expanding of social networks such as Instagram,
Facebook, MySpace, Twitter, and Sina Weibo, etc.
spammers on them are getting increasingly rampant.
Social spammers consistently make a mass of phony
records to misdirect the users and lead them to
malicious websites and illegal content. This research is
highlight features for perceiving spammers on
Facebook with the help of different classifiers. Also
compare the performance of different Machine
Learning Algorithms (MLA) like Support Vector
Machine (SVM), Multilayer Perceptron (MLP), K
Nearest Neighbor (KNN) and Random Forest (RF) on
machine learning tools WEKA and Rapid Miner. We
use the primary data collection technique to collect the
user profile data of Facebook. Lebel the data “Spam”
and “Not Spam” on the basis of Engagement Rate (ER),
Duplication Profile Picture and Not Human Name. The
outcomes of Support Vector Machine (SVM) from the
experiments is better than other algorithms on both
Machine Learning Tools (MLT) WEKA and
RapidMiner. The results of all algorithms are better
using WEKA as compare to RapidMiner. The results
will be valuable for researchers who are eager to build
machine learning models to recognize spamming
exercises on social media networks.
Keywords:- malicious content, spammers, machine
learning algorithms, Multilayer Perceptron, Random
Forest, K-Nearest Neighbor, Support Vector Machine,
RapidMiner, WEKA.
In recent time, social media have been
affected many undesirable threats. Social media
provided us an open platform to connect and share our
life events with others. Social media also attracted the
attentions of the spammers. Spam in social media
relates to undesirable, malicious and spontaneous
content, shown in different ways including malicious
links, massages, fake friends and microblogs, etc. With
the expanding of social networks such as Instagram,
Facebook, MySpace, Twitter, and Sina Weibo, etc.
spammers on them are getting increasingly rampant.
Social spammers consistently make a mass of phony
records to misdirect the users and lead them to
malicious websites and illegal content. This research is
highlight features for perceiving spammers on
Facebook with the help of different classifiers. Also
compare the performance of different Machine
Learning Algorithms (MLA) like Support Vector
Machine (SVM), Multilayer Perceptron (MLP), K
Nearest Neighbor (KNN) and Random Forest (RF) on
machine learning tools WEKA and Rapid Miner. We
use the primary data collection technique to collect the
user profile data of Facebook. Lebel the data “Spam”
and “Not Spam” on the basis of Engagement Rate (ER),
Duplication Profile Picture and Not Human Name. The
outcomes of Support Vector Machine (SVM) from the
experiments is better than other algorithms on both
Machine Learning Tools (MLT) WEKA and
RapidMiner. The results of all algorithms are better
using WEKA as compare to RapidMiner. The results
will be valuable for researchers who are eager to build
machine learning models to recognize spamming
exercises on social media networks.
Keywords:- malicious content, spammers, machine
learning algorithms, Multilayer Perceptron, Random
Forest, K-Nearest Neighbor, Support Vector Machine,
RapidMiner, WEKA.
