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Analysis of Users Behavior on Micro-blogging Site Using a Topic

  • Deepanshu BharadwajEmail author
  • Aprna Tripathi
  • Aman Agrawal
  • Mohd. Aamir Khan
Conference paper
  • 62 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1141)

Abstract

体育赛事投注记录peoples are generally influenced what everyone else is saying. a person observes a thing according to their nature, whether they are positive or negative kind of person. when these persons tweet other persons influenced by their thoughts. social media is nowadays a huge platform to spread a news. in this paper, we proposed a method to identify the user’s behavior on the micro-blogging site twitter. tweets are extracted according to the topic followed by the users behavior is analyzed according to their previous tweets. this method can be used in many ways to stop spamming on social media.

Keywords

Tweet analysis Tweet user behavior analysis User behavior 

References

  1. 1.
    Atodiresei, C.S., Tănăselea, A., Iftene, A.: Identifying fake news and fake users on twitter. Procedia Comput. Sci. 126, 451–461 (2018)
  2. 2.
    Chakra, A., Harpur, L., Rice, J.: Association of an emotional influencer to a post in a social medium (2019). US Patent 10,380,254
  3. 3.
    Gannon, W.: Build a sentiment analysis tool for twitter with this simple python script (2019).
  4. 4.
    Jayasekara, D.: Extracting twitter data, pre-processing and sentiment analysis using python 3.0 (2019).
  5. 5.
    Li, Y., Huang, J., Wang, H., Feng, L.: Predicting teenager’s future stress level from micro-blog. In: 2015 IEEE 28th International Symposium on Computer-Based Medical Systems, pp. 208–213. IEEE (2015)
  6. 6.
    Mogadala, A., Varma, V.: Twitter user behavior understanding with mood transition prediction. In: Proceedings of the 2012 workshop on Data-driven user behavioral modelling and mining from social media, pp. 31–34. ACM (2012)
  7. 7.
    Neto, V.R., Medeiros, D.S., Campista, M.E.M.: Analysis of mobile user behavior in vehicular social networks. In: 2016 7th International Conference on the Network of the Future (NOF), pp. 1–5. IEEE (2016)
  8. 8.
    Sa, B.P., Davis, S.M., Vilimonovic, N., Tan, J.Y., Goldsmid, A.P.: Searching for ideograms in an online social network (2018). US Patent App. 10/102,295
  9. 9.
    Shah, U., Finin, T., Joshi, A., Cost, R.S., Matfield, J.: Information retrieval on the semantic web. In: Proceedings of the eleventh international conference on Information and knowledge management, pp. 461–468. ACM (2002)
  10. 10.
    Tien, S.: Top twitter demographics that matter to social media marketers (2019).
  11. 11.
    Verma, P.K., Agarwal, S., Khan, M.A.: Opinion mining considering roman words using jaccard similarity algorithm based on clustering. In: 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–4. IEEE (2017)
  12. 12.
    Vitkova, L., Saenko, I., Tushkanova, O.: An approach to creating an intelligent system for detecting and countering inappropriate information on the internet. In: International Symposium on Intelligent and Distributed Computing, pp. 244–254. Springer (2019)

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Deepanshu Bharadwaj
    • 1
    Email author
  • Aprna Tripathi
    • 2
  • Aman Agrawal
    • 1
  • Mohd. Aamir Khan
    • 2
  1. 1.GL Bajaj Group of InstitutionsMathuraIndia
  2. 2.GLA UniversityMathuraIndia

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