Skip to main content
SpringerLink
Log in
Book cover

International Conference on Mining Intelligence and Knowledge Exploration

MIKE 2017: Mining Intelligence and Knowledge Exploration pp 391–401Cite as

Identifying Terrorist Index (T+) for Ranking Homogeneous Twitter Users and Groups by Employing Citation Parameters and Vulnerability Lexicon

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10682))

Abstract

Twitter, one of the important social media also acts as substrate to mix enzymes of actions and reactions of public sentiments including terrorist activities, either implicitly or explicitly. The identification of terrorist or crime related texts from Twitter is difficult because of the presence of such implicit and implied code-mixed hints that are solely shared among different users within a few groups. In the present task, we have developed a framework that deals with Twitter data and provides varieties of dataset useful for tracking various terrorist activities. The system also identifies the terrorist indices of different users as well as their groups. Compared to the available citation indices (e.g., h-index or i10-index), this newly proposed index takes into account the normalized parameters of citation and seed as well as code words related to crime from vulnerability lexicon. Finally, this index is used for ranking the Twitter users and groups and it is observed that our proposed ranking algorithms have performed reasonably better when the algorithms encapsulate the roles of citation and vulnerability parameters together.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://twitter.com/.

  2. 2.

    https://myvocabulary.com/word-list/terrorism-vocabulary/.

  3. 3.

    https://gist.github.com/jm3/2815378.

  4. 4.

    https://wordnet.princeton.edu/.

  5. 5.

    https://rednoise.org/rita/.

  6. 6.

    https://m.rediff.com/news/report/11-code-words-terrorists-in-india-use-a-lot/20140224.htm.

  7. 7.

    https://storymaps.esri.com/stories/terrorist-attacks/?year=2017.

  8. 8.

    https://Twitter4j.org/en/.

  9. 9.

    https://jsoup.org/.

  10. 10.

    http://libguides.cmich.edu/c.php?g=353669&p=2468209.

  11. 11.

    https://github.com/stanfordnlp/CoreNLP/blob/master/src/edu/stanford/nlp/process/Stemmer.java.

  12. 12.

    https://github.com/vatsan/gp-ark-tweet-nlp

  13. 13.

    http://guides.library.cornell.edu/c.php?g=32272&p=203388.

References

  1. Cambria, E., Rajagopal, D., Olsher, D., Das, D.: Big social data analysis. In: Akerkar, R., (ed.) Big Data Computing, Chap. 13, pp. 401–414. Taylor & Francis Group/CRC, Boca Raton (2015). Hardback: 978-1-46-657837-1, eBook: 978-1-46-657838-8.2013

    Google Scholar 

  2. Gupta, A., Kumaraguru, P.: Credibility ranking of tweets during high impact events. In: Proceedings of the 1st Workshop on Privacy and Security in Online Social Media, 17 April 2012, p. 2. ACM (2012)

    Google Scholar 

  3. Ravikumar, S., Balakrishnan, R., Kambhampati, S.: Ranking tweets considering trust and relevance. In: Proceedings of the Ninth International Workshop on Information Integration on the Web, 20 May 2012, p. 4. ACM (2012)

    Google Scholar 

  4. Huang, H., Zubiaga, A., Ji, H., Deng, H., Wang, D., Le, H.K., Abdelzaher, T.F., Han, J., Leung, A., Hancock, J.P., Voss, C.R.: Tweet ranking based on heterogeneous networks. In: COLING 2012, pp. 1239–1256 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, India

    Soumyadeep Debnath & Bappaditya Das

  2. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Dipankar Das

Authors
  1. Soumyadeep Debnath
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dipankar Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bappaditya Das
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dipankar Das .

Editor information

Editors and Affiliations

  1. Indian Statistical Institute, Kolkata, India

    Ashish Ghosh

  2. Institute for Development and Research in Banking Technology, Hyderabad, India

    Rajarshi Pal

  3. Indian Institute of Information Technology, Sri City, India

    Rajendra Prasath

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Debnath, S., Das, D., Das, B. (2017). Identifying Terrorist Index (T+) for Ranking Homogeneous Twitter Users and Groups by Employing Citation Parameters and Vulnerability Lexicon. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-71928-3_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71927-6

  • Online ISBN: 978-3-319-71928-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation