TY - GEN
T1 - Classifying twitter spammer based on user's behavior using decision tree
AU - Fitriani, Yuli
AU - Sumpeno, Surya
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4/18
Y1 - 2019/4/18
N2 - Twitter is one of microblogging service that widely used by people. Its popularity invites spammers to disturb other users with a large number of spam tweets. Spammers send untrusted news, unwanted tweets to another twitter accounts to introduce a product and service, a job with high salary, promote a new website, spread advertise to generate sales that could harm other users. This paper collects a hundred accounts from non-spammer and spammer. After that, manually classified as a non-spammer and spammer. User's behavior characteristics, which could give many clues to classify spammer. This paper applies profile users as features for the machine learning to classify users as a non-spammer or spammer. This paper applies seven attributes such as the statuses count, followers count, friends count, the age of account, average tweets per day, average limits between tweets, verified user or not. Using a Decision Tree method, we could classify non-spammer and spammer.
AB - Twitter is one of microblogging service that widely used by people. Its popularity invites spammers to disturb other users with a large number of spam tweets. Spammers send untrusted news, unwanted tweets to another twitter accounts to introduce a product and service, a job with high salary, promote a new website, spread advertise to generate sales that could harm other users. This paper collects a hundred accounts from non-spammer and spammer. After that, manually classified as a non-spammer and spammer. User's behavior characteristics, which could give many clues to classify spammer. This paper applies profile users as features for the machine learning to classify users as a non-spammer or spammer. This paper applies seven attributes such as the statuses count, followers count, friends count, the age of account, average tweets per day, average limits between tweets, verified user or not. Using a Decision Tree method, we could classify non-spammer and spammer.
KW - Classification
KW - Decision tree
KW - Machine learning
KW - Microblogging
KW - Spammers
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85100693587&partnerID=8YFLogxK
U2 - 10.1109/APCoRISE46197.2019.9318872
DO - 10.1109/APCoRISE46197.2019.9318872
M3 - Conference contribution
AN - SCOPUS:85100693587
T3 - 2019 Asia Pacific Conference on Research in Industrial and Systems Engineering, APCoRISE 2019
BT - 2019 Asia Pacific Conference on Research in Industrial and Systems Engineering, APCoRISE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 Asia Pacific Conference on Research in Industrial and Systems Engineering, APCoRISE 2019
Y2 - 18 April 2019 through 19 April 2019
ER -