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In the present generation, On-Line social networks (OSNs) have become increasingly popular, which impacts people’s social lives and impel them to become associated with various social media sites. Social Networks are the essential platforms through which many activities such as promotion, communications, agenda creation, advertisements, and news creation have started to be done. Adding new friends and keeping in contact with them and their updates has become easier. Researchers have been studying these online social networks to see the impact they make on the people. Some malicious accounts are used for
purposes such as misinformation and agenda creation. Detection of malicious account is significant. The methods based on machine learning-based were used to detect fake accounts that could mislead people. The dataset is pre-processed using various python libraries and a comparison model is obtained to get a feasible algorithm suitable for the given dataset. An attempt to detect fake accounts on the social media platforms is determined by various Machine Learning algorithms. The classification performances of the algorithms Random Forest, Neural Network and Support Vector Machines are used for the detection of fake accounts.

It has become quite unpretentious to obtain any kind of information from any source across the world by using the Internet. The increased demand of social sites permits users to collect abundant amount of information and data about users. Huge volumes of data available on these sites also draw the attention of fake users. Twitter has rapidly become an online source for acquiring real-time information about users. Twitter is an Online Social Network (OSN)where users can share anything and everything, such as news, opinions, and even their moods. Several arguments can be held over different topics, such as politics, current affairs, and important events. When a user tweets something, it is instantly conveyed to his/her followers, allowing them to outspread the received information at a much broader level .
With the evolution of OSNs, the need to study and analyze users‟ behaviors in online social platforms has intensified . Many people who do not have much information regarding the OSN scan easily be tricked by the fraudsters. There is also a demand to combat and place a control on the people who use OSNs only for advertisements and thus spam other people‟s accounts. Recently, the detection of spam in social networking sites attracted the attention of researchers. Spam detection is a difficult task in maintaining the security of social networks. It is essential to recognize spams in the OSN sites to save users from various kinds of malicious attacks and to preserve their security and privacy. These hazardous maneuvers adopted by spammers cause massive destruction of the community in the real world. Twitter spammers have various objectives, such as spreading invalid information, fake news, rumors, and spontaneous messages. Spammers achieve their malicious objectives through advertisements and several other means where they support different mailing lists and subsequently dispatch spam messages randomly to broadcast their interests. These activities cause disturbance to the original users who are known as non-spammers. In addition, it also decreases the repute of the OSN platforms. Therefore, it is essential to design a scheme to spot spammers so that corrective efforts can be taken to counter their malicious activities. Several research works have been carried out in the domain of Twitter spam detection. To encompass the existing state-of the-art, a few surveys have also been carried out on fake user identification from Twitter. Tingmin et al. Provide a survey of new methods and techniques to identify Twitter spam detection. The survey presents a comparative study of the current approaches. On the other hand, the authors in conducted a survey on different behaviors exhibited by spammers on Twitter social network. The study also provides a literature review that recognizes the existence of spammers on Twitter social network. Despite all the existing studies, there is still a gap in the existing literature. Therefore, to bridge the gap, we review state-of-the-art in the spammer detection and fake user identification on Twitter. Moreover, this survey presents taxonomy of the Twitter spam detection approaches and attempts to offer a detailed description of recent developments in the domain. The aim of this project is to identify different approaches of spam detection on Twitter and to present a taxonomy by classifying these approaches into several categories. For classification, we have identified four means of reporting spammers that can be helpful in identifying fake identities of users. Spammers can be identified based on: (i) fake content, (ii) URL based spam detection, (iii) detecting spam in trending topics, and (iv)fake user identification

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