体育赛事投注记录

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An Analysis of Machine Learning Approach for Detecting Automated Spammer in Twitter

  • C. VanmathiEmail author
  • R. Mangayarkarasi
Conference paper
  • 21 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 127)

Abstract

体育赛事投注记录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.

Keywords

Twitter Spammer detection Machine learning 

References

  1. 1.
    Fazil M, Abulaish M (2018) A hybrid approach for detecting automated spammers in Twitter. IEEE Trans Inform Forensics Secur 13(11)
  2. 2.
    Amleshwaram AA, Reddy N, Yadav S, Gu G, Yang C (2013) CATS: characterizing automation of Twitter spammers. IEEE, Department of Electrical and Computer Engineering Texas A&M University
  3. 3.
    Talha A, Kara R (2017) A survey of spam detection methods on Twitter. Int J Adv Comput Sci Appl (IJACSA) 8(3)
  4. 4.
    Rajadesingan A, Zafarani R, Liu H (2015) Sarcasm detection on Twitter: a behavioral modeling approach. In: WSDM’15, Shanghai, China, 2–6 Feb 2015
  5. 5.
    Concone F, Lo Re G, Morana M, Ruocco C (2019) Twitter spam account detection by effective labelling. In: CEUR-WS.org/Vol-2315
  6. 6.
    Chatzakouy D, Kourtellisz N, Blackburnz J, De Cristofaro E, Stringhini G, Vakali A (2017) Mean birds: detecting aggression and bullying on Twitter, 12 May 2017
  7. 7.
    Martinez-Romo J, Araujo L (2012) Detecting malicious tweets in trending topics using a statistical analysis of language. Elsevier, Amsterdam
  8. 8.
    Shuy K, Slivaz A, Wangy S, Tang J, Liuy H (2017) Fake news detection on social media: a data mining perspective, 3 Sept 2017
  9. 9.
    Benhardus J, Kalita J (2013) Streaming trend detection in Twitter. Int J Web Based Commun 9(1)
  10. 10.
    Vishwarupe V, Bedekar M, Pande M, Hiwale A (2018) Intelligent Twitter spam detection: a hybrid approach, Jan 2018
  11. 11.
    Fernandes MA, Patel P, Marwala T (2015) Automated detection of human users in Twitter. Procedia Comput Sci 53:224–231
  12. 12.
    Washhaa M, Qaroushb A, Mezghania M, Sedesa F (2017) A topic-based hidden Markov model for real-time spam tweets filtering
  13. 13.
    DeBarr D, Wechsler H (2012) Spam detection using random boost, 24 Mar 2012
  14. 14.
    Delany SJ, Buckley M, Greene D (2012) SMS spam filtering: methods and data. Expert Syst Appl 39
  15. 15.
    Bajaj S, Garg N, Singh SK (2017) A novel user-based spam review detection. Procedia Comput Sci 122
  16. 16.
    Naem AA, Ghali NI, Saleh AA (2018) Antlion optimization and boosting classifier for spam email detection. Future Comput Inform J 3
  17. 17.
    Biggio B, Fumera G, Pillai I, Roli F (2011) A survey and experimental evaluation of image spam filtering techniques. Pattern Recogn Lett 32
  18. 18.
    Farisa H, Al-Zoubia AM, Heidarib AA, Aljaraha I, Mafarja M, Hassonaha MA, Fujitad H (2019) An intelligent system for spam detection and identification of the most relevant features based on evolutionary Random Weight Networks. Inform Fusion 48
  19. 19.
    Inuwa-Dutse I, Liptrott M, Korkontzelos I (2018) Detection of spam-posting accounts on Twitter. Neurocomputing 315
  20. 20.
    Wang B, Chen G, Fu L, Song L, Wang X, Liu X (2016) DRIMUX: dynamic rumor influence minimization with user experience in social networks
  21. 21.
    Arkalgud N (2018) Logistic regression for spam filtering, 14 Feb 2008

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringVIT UniversityVelloreIndia

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