Analysis and Classification of Crime Tweets

Sangeeta Lal, Lipika Tiwari, Ravi Ranjan, Ayushi Verma, Neetu Sardana, Rahul Mourya

Research output: Contribution to journalConference articlepeer-review

26 Citations (Scopus)
127 Downloads (Pure)


Nowadays social Networking and micro-blogging sites like Twitter are very popular and millions of users are registered on these websites. The users present on these website use these websites as a platform to express their thoughts and opinions. Our analysis of content posted on Twitter shows that users often post crime related information on Twitter. Among these crime related tweets some tweets are the crime messages that need police attention. Detection of such tweets can be beneficial in utilizing pattroling resources. The analysis of the data present on these websites can have an enormous impact. In this paper,the work is done on analyzing Twitter data to identify crime tweet that need police attention. Text mining based approach is used for classification of 369 tweets into crime and not-crime class. Classifiers such as Naive Bayesian, Random Forest, J48 and ZeroR are used. Among all of these four classifiers, Random forest classifier give the best accuracy of 98.1%.

Original languageEnglish
Pages (from-to)1911-1919
Number of pages9
JournalProcedia Computer Science
Early online date16 Apr 2020
Publication statusPublished - 2020
Event2019 International Conference on Computational Intelligence and Data Science - Gurugram, India
Duration: 6 Sept 20197 Sept 2019


  • Crime Detection
  • J48
  • Random Forest
  • Twitter
  • ZeroR

ASJC Scopus subject areas

  • Computer Science(all)


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