Detecting Spam Accounts on Twitter

Detecting Spam Accounts on Twitter. Alom, M. Z., Carminati, B., & Ferrari, E. In Brandes, U., Reddy, C., & Tagarelli, A., editors, IEEE/ACM 2018 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018, Barcelona, Spain, August 28-31, 2018, pages 1191–1198, 2018. IEEE Computer Society.

Paper doi abstract bibtex

Social networks have become a popular way for internet surfers to interact with friends and family members, reading news, and also discuss events. Users spend more time on well-known social platforms (e.g., Facebook, Twitter, etc.) storing and sharing their personal information. This information together with the opportunity of contacting thousands of users attract the interest of malicious users. They exploit the implicit trust relationships between users in order to achieve their malicious aims, for example, create malicious links within the posts/tweets, spread fake news, send out unsolicited messages to legitimate users, etc. In this paper, we investigate the nature of spam users on Twitter with the goal to improve existing spam detection mechanisms. For detecting Twitter spammers, we make use of several new features, which are more effective and robust than existing used features (e.g., number of followings/followers, etc.). We evaluated the proposed set of features by exploiting very popular machine learning classification algorithms, namely k-Nearest Neighbor (k-NN), Decision Tree (DT), Naive Bayesian (NB), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XG-Boost). The performance of these classifiers are evaluated and compared based on different evaluation metrics. We compared the performance of our proposed approach with four latest state of art approaches. The experimental results show that the proposed set of features gives better performance than existing state of art approaches.

@inproceedings{DBLP:conf/asunam/AlomCF18,
title = {Detecting Spam Accounts on Twitter},
author = {Md. Zulfikar Alom and Barbara Carminati and Elena Ferrari},
editor = {Ulrik Brandes and Chandan Reddy and Andrea Tagarelli},
url = {https://doi.org/10.1109/ASONAM.2018.8508495},
doi = {10.1109/ASONAM.2018.8508495},
year  = {2018},
date = {2018-01-01},
booktitle = {IEEE/ACM 2018 International Conference on Advances in Social Networks 
 Analysis and Mining, ASONAM 2018, Barcelona, Spain, August 28-31, 
 2018},
pages = {1191--1198},
publisher = {IEEE Computer Society},
abstract = {Social networks have become a popular way for internet surfers to interact with friends and family members, reading news, and also discuss events. Users spend more time on well-known social platforms (e.g., Facebook, Twitter, etc.) storing and sharing their personal information. This information together with the opportunity of contacting thousands of users attract the interest of malicious users. They exploit the implicit trust relationships between users in order to achieve their malicious aims, for example, create malicious links within the posts/tweets, spread fake news, send out unsolicited messages to legitimate users, etc. In this paper, we investigate the nature of spam users on Twitter with the goal to improve existing spam detection mechanisms. For detecting Twitter spammers, we make use of several new features, which are more effective and robust than existing used features (e.g., number of followings/followers, etc.). We evaluated the proposed set of features by exploiting very popular machine learning classification algorithms, namely k-Nearest Neighbor (k-NN), Decision Tree (DT), Naive Bayesian (NB), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XG-Boost). The performance of these classifiers are evaluated and compared based on different evaluation metrics. We compared the performance of our proposed approach with four latest state of art approaches. The experimental results show that the proposed set of features gives better performance than existing state of art approaches.},
keywords = {Spam detection; Twitter; Machine learning; Social network security},
pubstate = {published},
tppubtype = {inproceedings}
}

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