Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8605
Title: Detecting spam tweets using machine learning and effective preprocessing
Authors: Kardaş B.
Bayar, İsmail Erdem
Özyer T.
Alhajj R.
Keywords: machine learning
preprocessing
social media
spam detection
Twitter
Learning algorithms
Logistic regression
Statistical tests
Support vector machines
Detection accuracy
Machine learning algorithms
Naive Bayes classifiers
Neural-networks
Phishing
Pre-processing techniques
Preprocessing
Social media
Spam detection
Spammers
Social networking (online)
Issue Date: 2021
Publisher: Association for Computing Machinery, Inc
Source: Kardaş, B., Bayar, İ. E., Özyer, T., & Alhajj, R. (2021, November). Detecting spam tweets using machine learning and effective preprocessing. In Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 393-398).
Abstract: Nowadays, with the rapid increase in popularity of online social networks (OSNs), these platforms are realized as ideal places for spammers. Unfortunately, these spammers can easily publish malicious content, advertise phishing scams by taking advantage of OSNs. Therefore, effective identification and filtering of spam tweets will be beneficial to both OSNs and users. However, it is becoming increasingly difficult to check and eliminate spam tweets due to this great flow of posts. Motivated by these observations, in this paper we propose an approach for the detection of spam tweets using machine learning and effective preprocessing techniques. The approach proposes the advantages of the preprocessing and which of these preprocessing techniques are the most effective. To compare these techniques UtkML Twitter spam dataset is used in testing. After the most effective methods determined, the detection accuracy of the spam tweets will be better optimized by combining them. We have evaluated our solution with four different machine learning algorithms namely - Naïve Bayes Classifier, Neural Network, Logistic Regression and Support Vector Machine. With SVM Classifier, we are able to achieve an accuracy of 93.02%. Experimental results show that our approach can improve the performance of spam tweet classification effectively. © 2021 ACM.
Description: ACM Special Interest Group on Knowledge Discovery in Data (SIGKDD);Elsevier;IEEE Computer Society;IEEE TCDE;Springer
13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021 -- 8 November 2021 -- 176732
URI: https://doi.org/10.1145/3487351.3490968
https://hdl.handle.net/20.500.11851/8605
ISBN: 9781450391283
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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