Feature engineering for detecting spammers on Twitter: Modelling and analysis

Abstract

Twitter is a social networking website that has gained a lot of popularity around the world in the last decade. This popularity made Twitter a common target for spammers and malicious users to spread unwanted advertisements, viruses and phishing attacks. In this article, we review the latest research works to determine the most effective features that were investigated for spam detection in the literature. These features are collected to build a comprehensive data set that can be used to develop more robust and accurate spammer detection models. The new data set is tested using popular classifiers (Naive Bayes, support vector machines, multilayer perceptron neural networks, Decision Trees, Random forests and k-Nearest Neighbour). The prediction performance of these classifiers is evaluated and compared based on different evaluation metrics. Moreover, a further analysis is carried out to identify the features that have higher impact on the accuracy of spam detection. Three different techniques are used and compared for this analysis: change of mean square error (CoM), information gain (IG) and Relief-F method. Top five features identified by each technique are used again to build the detection models. Experimental results show that most of the developed classifiers obtained high evaluation results based on the comprehensive data set constructed in this work. Experiments also reveal the important role of some features like the reputation of the account, average length of the tweet, average mention per tweet, age of the account, and the average time between posts in the process of identifying spammers in the social network.

Spam profiles detection on social networks using computational intelligence methods: The effect of the lingual context

“…Both regulations and technical solutions are necessary to stop spammers and retain the users’ trust in their platforms. As spam diminishes the productivity, trustworthiness and performance of the OSNs, causing to expose the users to different security threats, it is considered very important to mitigate or to totally eliminate spam profiles [18]. In our previous work [19], the analysis was based on the algorithm itself, unlike this work which focuses more on the analytical side of the features.…”

“…This indicates that the German users are content-based, while the Indonesian and Malay tend to be more social and message broadcasting–based. A growing body of research is targeting social spam with the goal to improve spam detection methods in the context of OSNs [18,25]. However, there is less focus in the literature on understanding the social spam in different lingual contexts in OSNs.…”

XFake: Explainable Fake News Detector with Visualizations

“…", Whether contains the mark "!" [3]. For each news item in the training set, we extract its linguistic features and train an XGBoost [1] classifier using these features.…”

An Effective Identification Technology for Online News Comment Spammers in Internet Media

“…Therefore, the identification technology of spammers has been extensively studied. According to different areas, the current research areas on spammer identification technologies can be divided into the e-mail field, the field of social networks, the field of news media, the e-commerce field [20]- [22]. According to different research methods, it can be divided into spammer identification technologies based on user behavioral features, semantic features, and environmental features [28], [29].…”