With every passing second social network
community is growing rapidly, because of that, attackers
have shown keen interest in these kinds of platforms and
want to distribute mischievous contents on these
platforms. With the focus on introducing new set of
characteristics and features for counteractive measures, a
great deal of studies has researched the possibility of
lessening the malicious activities on social media networks.
This research was to highlight features for identifying
spammers on Instagram and additional features were
presented to improve the performance of different
machine learning algorithms. Performance of different
machine learning algorithms namely, Multilayer
Perceptron, Random Forest, K-Nearest Neighbor and
Support Vector Machine were evaluated on machine
learning tools named, RapidMiner and WEKA. The result
from this research tells us that Random Forest
outperformed all other selected machine learning
algorithms on both selected machine learning tools.
Overall, Random Forest provided best results on
RapidMiner. These results are useful for the researchers
who are keen to build machine learning models to find out
the spamming activities on social network communities.
Keywords : Malicious Activities, Spammers, Machine Learning Algorithms, Multilayer Perceptron, Random Forest, K-Nearest Neighbor, Support Vector Machine, Rapidminer, WEKA, Social Network Communities.