Spam Detection in Twitter using Machine Learning Algorithms
S. Jeyapriyanga1, B. Mahalakshmi2, Anuradha C3
1S.Jeyapriyanga, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
2B.Mahalakshmi, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
3Anuradha C, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
Manuscript received on 13 September 2019 | Revised Manuscript received on 22 September 2019 | Manuscript Published on 10 October 2019 | PP: 174-178 | Volume-8 Issue-6S2, August 2019 | Retrieval Number: F10460886S219/19©BEIESP | DOI: 10.35940/ijeat.F1046.0886S219
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Twitter is being one of the most generally utilized interpersonal organizations on the planet which has been a key objective for interlopers. In this work, Identifying spammers in twitter System is to be proposed which isolate the spammers tweets among specialists tweets by distinguishing and recognizing twitter messages. Here the emphasis depends on the tweet level spammer recognition. This work is a methodology for recognizing spammer tweets among specialists tweets utilizing three classifiers, for example, Best First Decision Tree, K Nearest Neighbor. This thusly prompts better tweet characterization. The thing to be considered is the preparation information is created naturally as master tweets and spammers tweets. It is finished by investigating the tweets and extricating watchwords. The HITS calculation is utilized to rank the spammers. Three classifiers here are utilized to characterize the tweets and casting a ballot strategy is utilized to mark the most extreme estimations of the tweets which has been arranged by the classifiers.
Keywords: Twitter, Spam Tweets, Ham Tweets, Detector, Ensemble, Classifier, Blacklist, Machine Learning.
Scope of the Article: Machine Learning