Book ChapterDOI
Intelligent Twitter Spam Detection: A Hybrid Approach
Varad Vishwarupe,Mangesh Bedekar,Milind Pande,A. S. Hiwale +3 more
- pp 189-197
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TLDR
An Intelligent Twitter Spam Detection System which gives the precise details about spam profiles by identifying and detecting twitter spam by taking into account some unique feature sets before analyzing the tweets.Abstract:
Over the years there has been a large upheaval in the social networking arena. Twitter being one of the most widely-used social networks in the world has always been a key target for intruders. Privacy concerns, stealing of important information and leakage of key credentials to spammers has been on the rise. In this paper, we have developed an Intelligent Twitter Spam Detection System which gives the precise details about spam profiles by identifying and detecting twitter spam. The system is a Hybrid approach as opposed to single-tier, single-classifier approaches which takes into account some unique feature sets before analyzing the tweets and also checks the links with Google Safe Browsing API for added security. This in turn leads to better tweet classification and improved as well as intelligent twitter spam detection.read more
Citations
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Journal ArticleDOI
Evolving Support Vector Machines using Whale Optimization Algorithm for spam profiles detection on online social networks in different lingual contexts
TL;DR: A hybrid machine learning model based on Support Vector Machines and one of the recent metaheuristic algorithms called Whale Optimization Algorithm is proposed for the task of identifying spammers in online social networks and provides very challenging results in terms of precision, recall, f-measure and AUC.
Journal ArticleDOI
Spam review detection using spiral cuckoo search clustering method
TL;DR: A spiral cuckoo search based clustering method has been introduced to discover spam reviews and the experimental results and statistical analysis validate that the proposed method outruns the existing methods.
Journal ArticleDOI
Spam profiles detection on social networks using computational intelligence methods: The effect of the lingual context:
TL;DR: The nature and the characteristics of spam profiles in a social network like Twitter to improve spam detection, based on a number of publicly available language-independent features, are addressed, leading to a better understanding of social spam and improving detection methods by considering the various important features resulting from the different lingual contexts.
Journal ArticleDOI
A hybrid classification method for Twitter spam detection based on differential evolution and random forest
TL;DR: A hybrid method, which is based on Synthetic Minority Over‐sampling TEchnique (SMOTE) and Differential Evolution (DE) strategies, is presented to enhance the spam detection rate in real Twitter datasets.
Journal ArticleDOI
A Spammer Identification Method for Class Imbalanced Weibo Datasets
TL;DR: By analyzing the characteristics of spammers in Weibo, an ensemble learning method is used to combine multiple base classifiers for improving the learning performance and demonstrates that compared with the existing state-of-the-art methods, the recall rate of the proposed approach increases by 6.5% and reaches the precision value of 87.53% when used to deal with real-world Weibo datasets.
References
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Battling the Internet Water Army: Detection of Hidden Paid Posters
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Proceedings ArticleDOI
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