Abstract
With the evolution of social media, information sharing in online is turning into ubiquitous each and every day. Various facts are propagating thru online social networks along with each the wonderful and negative. In this paper, our essential focal point is to resolve terrible or incorrect data such as rumors. Rumor is a serious problem in online social media. The wrong rumors can create many problems in society; hence, it is important to block these rumors. First in this paper, we have compared Naive Bayes algorithm and logistic regression for deciding which algorithm is better for spam classification. Then, we have proposed a model for detecting spammers in twitters and automatically blocking those rumors by hindering an assured subset of the nodes. In order to decrease the influence of the rumors, nodes are allotted an acceptance time threshold. If a user posts the rumor more than a particular threshold, the user gets automatically blocked. We have created a Java-based Web application using tomcat SQL server, JavaScript html and CSS, like Facebook where a user can search for friends, accept friend requests, chat with them, change profile pictures and post messages. In this paper, we used a supervised learning technique known as Naive Bayes Algorithm for blocking of the users. The analysis of users blocked per month can be also done which will help in study of users and rumor details.
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Vanmathi, C., Mangayarkarasi, R. (2021). An Analysis of Machine Learning Approach for Detecting Automated Spammer in Twitter. In: Tripathy, A., Sarkar, M., Sahoo, J., Li, KC., Chinara, S. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 127. Springer, Singapore. https://doi.org/10.1007/978-981-15-4218-3_43
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DOI: https://doi.org/10.1007/978-981-15-4218-3_43
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