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Author: Alex Hai Wang

Affiliation: The Pennsylvania State University, United States

Keyword(s): Social network security, Spam detection, Machine learning, Classification.

Related Ontology Subjects/Areas/Topics: Data and Application Security and Privacy ; Information and Systems Security ; Phishing, Adfraud, Malware, and Countermeasures ; Security and Privacy in Social Networks

Abstract: The rapidly growing social network Twitter has been infiltrated by large amount of spam. In this paper, a spam detection prototype system is proposed to identify suspicious users on Twitter. A directed social graph model is proposed to explore the “follower” and “friend” relationships among Twitter. Based on Twitter’s spam policy, novel content-based features and graph-based features are also proposed to facilitate spam detection. A Web crawler is developed relying on API methods provided by Twitter. Around 25K users, 500K tweets, and 49M follower/friend relationships in total are collected from public available data on Twitter. Bayesian classification algorithm is applied to distinguish the suspicious behaviors from normal ones. I analyze the data set and evaluate the performance of the detection system. Classic evaluation metrics are used to compare the performance of various traditional classification methods. Experiment results show that the Bayesian classifier has the best ove rall performance in term of F-measure. The trained classifier is also applied to the entire data set. The result shows that the spam detection system can achieve 89% precision. (More)

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Paper citation in several formats:
Hai Wang, A. (2010). DON’T FOLLOW ME - Spam Detection in Twitter. In Proceedings of the International Conference on Security and Cryptography (ICETE 2010) - SECRYPT; ISBN 978-989-8425-18-8; ISSN 2184-3236, SciTePress, pages 142-151. DOI: 10.5220/0002996201420151

@conference{secrypt10,
author={Alex {Hai Wang}.},
title={DON’T FOLLOW ME - Spam Detection in Twitter},
booktitle={Proceedings of the International Conference on Security and Cryptography (ICETE 2010) - SECRYPT},
year={2010},
pages={142-151},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002996201420151},
isbn={978-989-8425-18-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the International Conference on Security and Cryptography (ICETE 2010) - SECRYPT
TI - DON’T FOLLOW ME - Spam Detection in Twitter
SN - 978-989-8425-18-8
IS - 2184-3236
AU - Hai Wang, A.
PY - 2010
SP - 142
EP - 151
DO - 10.5220/0002996201420151
PB - SciTePress

- Science and Technology Publications, Lda.
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