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MACHINE LEARNING TECHNIQUES FOR SPAMMER IDENTIFICATION: STATE OF THE ART AND ANALYSIS

Internet offers seamless communication with a high consumption rate. With the increase in the widespread utilization of internet tools such as e-mails, the spammers started to exploit the e-mail network to effect malicious and hazardous activities. As the e-mail spam detection systems became more sophisticated and accurate, the attention of spammers has now turned to the recently emerged social networks due to massive usage and popularity among the people. A range of online social networks (OSN) such as Facebook, Twitter, Instagram are available with each of them offering unique services to the account owners and benefits both professionally and personally. Besides these advantages, the network also houses illicit accounts that disturbs the user experience and destroy the ultimate objective of social networking. The existing techniques employed by these OSN to detect such illicit users are not effective and accurate and also demands manual approaches to spot them. Hence, a number of methods and algorithms were proposed in the literature to identify the spammers concealed in these networks. This work attempts to provide a detailed review of state of the art techniques and methodologies employed in the spam account detection problem along with future research directions.


INTRODUCTION
With the advent of online internet communication, businesses have started adopting various online platforms as a tool for branding, promotions and sales. Diverse fields such as movies, hotels, televisions, photography, arts, and job portals make use of these online medium to satisfy its needs with respect to showcasing of products or services. The internet enables people to stay connected irrespective of time and space.
However, with the coming of recently popular social networks, the people started spending most of their time to simply express themselves, their whereabouts and share information with the network of family and friends. It has almost become an integral part of everyday life.
The way a social media is being used differs from person to person. The expectations and anticipations of users from social media tools are different, and hence possess diverse uses as well. The social media is emerging as a platform where once can socialize, display works and get appreciated and an expression of self.
In this study we have analysed the existing concept and methods for the detection of spammers i.e. the spam accounts on most popular social media sites such as Twitter, Facebook, Instagram and YouTube. Malicious users fool the genuine users by posting advertisements, phishing sites, fraud, pornography, viruses, etc. under the comments section of popular videos of the time. The spammers initiate several hazardous activities to exploit the network. Spams on Twitter not only affect the online social experience, but also threatens the safety of cyberspace.
Therefore, there is an urgent need to effectively combat the spread of spams. The spamming issues have attracted the attention of the research community. Researchers have put many efforts to improve social spam detection in terms of efficiency and accuracy by proposing various novel approaches.
Spam message detection and spam account detection are two different problems that are being researched in the social media domain. There exists a very few works on spam account detection and hence calls for further researching to better the techniques and keep the environment safe and appropriate for the purpose it has been intended to do.
This work aims at throwing a light on the state-of-the-art methods and techniques on the spammers' identification (spam account detection) that also includes the recent advancements and merits and demerits of each of these methods.
The analysis made will give rise to new ideas and concepts to tackle this problem. Spam Detection framework is a binary classification problem that classifies an account as spam or non-spam. A machine learning spam account detection model usually comprises of two phases as follows: (i) Training phase: It is the first phase in which detection model is trained using classified labelled samples. (ii) Testing phase: It is a follow up of training phase. Unlabelled samples are tested and generate the results by classifying each sample into spam or non-spam class. The rest of the paper is organized as follows: The Section II describes the state-of-the-art of spam detection problem. Section III summarizes the methodology followed in the process of spam account identification and Section IV concludes the work.

SPAM DETECTION METHODS EMPLOYED IN THE LITERATURE
While researching the works done by various authors, it was evident that most of the works concentrate on spam message detection and only a few works exist on spam account or profile detection. Hence, there is a huge research vacuum that prevails in this domain. One of the most challenging parts of the research is the availability of datasets and distribution of the same. Though several social networks exist for day-to-day use, maximum of the researches have been conducted considering the Twitter social network. There are other works on social networks such as fake review identification, fake profile identification, social bot classification, user-sentiment analysis and spam tweet removal.

METHODOLOGY
The basic requirement to undergo research in social media is the availability of datasets. Unfortunately, due to the social networks' policy on data sharing, the availability of datasets to pursue research does not exist. Hence, the researchers have crawled the social networks' data using API's and used them for experimentation. A range of features categories have been proposed in the literature as shown in the Fig. 1.

Fig. 1: Categories of Feature Sets
The above is a consolidation of the feature sets employed in the literature. However, not all the features have been used in any of the study. Few or more categories were proposed and newer features were proposed for the feature bunches. Though Precision, Recall, F-Measure, Accuracy, True Positive Rate, False Positive Rate, ROC and AUC have been used for model evaluation, the training and testing time have also been used to check for the quickness in categorizing the profiles.

CONCLUSION AND RESEARCH DIRECTIONS
This paper provided a deep analysis and insights on the state-of-the art techniques existing for the spam profile identification. The paper comprises of the recent works that also include the pros and cons of the methodologies proposed in the literature. The size of the dataset employed for the previous researches were not sufficient to generalize to that of a social network that consists of enormous amount of data. Secondly, the social networks are of growing in nature and so the methods should be devised that could address the scalability of networks. Finally, instead of building models for exclusive social networks, a possibility is that a unified approach could be devised through which the system would be able to detect the spammers irrespective of the kind of social network it belongs to.