Skip to main content
SpringerLink
Log in

Identifying Differentiating Factors for Cyberbullying in Vine and Instagram

  • Conference paper
  • First Online:
Information Management and Big Data (SIMBig 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1410))

Included in the following conference series:

Abstract

A multitude of online social networks (OSNs) of varying types has been introduced in the past decade. Because of their enormous popularity and constant availability, the threat of cyberbullying launched via these OSNs has reached an unprecedented level. Victims of cyberbullying are now more vulnerable than ever before to the predators, perpetrators, and stalkers. In this work, we perform a detailed analysis of user postings on Vine and Instagram social networks by making use of two labeled datasets. These postings include threads of media posts and user comments that were labeled for being cyberbullying instances or not. Our analysis has revealed several important differentiating factors between cyberbullying and non-cyberbullying instances in these social networks. In particular, cyberbullying and non-cyberbullying instances differ in (i) the number of unique negative commenters, (ii) temporal distribution of positive and negative sentiment comments, and (iii) textual content of media captions and subsequent comments. The results of these analyses can be used to build highly accurate classifiers for identifying cyberbullying instances.

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

Access this chapter

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

Institutional subscriptions

References

  1. Arslan, P., Corazza, M., Cabrio, E., Villata, S.: Overwhelmed by negative emotions? Maybe you are being cyber-bullied! In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, SAC 2019, pp. 1061–1063. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3297280.3297573

  2. Cheng, L., Li, J., Silva, Y.N., Hall, D.L., Liu, H.: XBully: cyberbullying detection within a multi-modal context. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 339–347. WSDM 2019. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3289600.3291037

  3. Dadvar, M., de Jong, F.M.G., Ordelman, R.J.F., Trieschnigg, R.B.: Improved cyberbullying detection using gender information. In: Twelfth Dutch-Belgian Information Retrieval Workshop, DIR, pp. 23–25. University of Ghent (2012)

    Google Scholar 

  4. Dinakar, K., Reichart, R., Lieberman, H.: Modeling the detection of textual cyberbullying (2011)

    Google Scholar 

  5. Goldman, R.: Teens indicted after allegedly taunting girl who hanged herself, bbc news (2010). http://abcnews.go.com/Technology/TheLaw/teens-charged-bullying-mass-girl-kill/story?id=10231357. Accessed 14 Jan 2014

  6. Hosseinmardi, H., Ghasemianlangroodi, A., Han, R., Lv, Q., Mishra, S.: Towards understanding cyberbullying behavior in a semi-anonymous social network. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Beijing, China, pp. 244–252. IEEE (2014)

    Google Scholar 

  7. Hosseinmardi, H., Rafiq, R.I., Han, R., Lv, Q., Mishra, S.: Prediction of cyberbullying incidents in a media-based social network. In: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, San Francisco, CA, USA. IEEE (2016)

    Google Scholar 

  8. Hosseinmardi, H., Rafiq, R.I., Han, R., Lv, Q., Mishra, S.: Prediction of cyberbullying incidents in a media-based social network. In: 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 186–192. IEEE (2016)

    Google Scholar 

  9. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)

    Google Scholar 

  10. Huang, Q., Singh, V.K., Atrey, P.K.: Cyber bullying detection using social and textual analysis. In: Proceedings of the 3rd International Workshop on Socially-Aware Multimedia, pp. 3–6. ACM, New York (2014). https://doi.org/10.1145/2661126.2661133. http://doi.acm.org/10.1145/2661126.2661133

  11. Huddleston, T.: Twitter is officially shutting down vine today (2017). https://fortune.com/2017/01/17/twitter-shut-down-vine-tuesday/. Accessed 28 July 2020

  12. Reynolds, K., Kontostathis, A., Edwards, L.: Using machine learning to detect cyberbullying. In: Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops, vol. 2, pp. 241–244 (2011). http://doi.ieeecomputersociety.org/10.1109/ICMLA.2011.152

  13. Kontostathis, A., Reynolds, K., Garron, A., Edwards, L.: Detecting cyberbullying: query terms and techniques. In: Proceedings of the 5th Annual ACM Web Science Conference, pp. 195–204. ACM (2013)

    Google Scholar 

  14. Kowalski, R.M., Limber, S., Limber, S.P., Agatston, P.W.: Cyberbullying: Bullying in the Digital Age. Wiley, Reading (2012)

    Google Scholar 

  15. Law, T.: Vine has a new successor: the 6-second video app byte (2020). https://time.com/5771854/vine-byte-app-launch/. Accessed 28 July 2020

  16. Li, H.H.S., Yang, Z., Lv, Q., Han, R.I.R.R., Mishra, S.: A comparison of common users across Instagram and ask.fm to better understand cyberbullying. In: 2014 IEEE Fourth International Conference on Big Data and Cloud Computing, Sydney, Australia, pp. 355–362. IEEE, December 2014. https://doi.org/10.1109/BDCloud.2014.87

  17. Nahar, V., Li, X., Pang, C.: An effective approach for cyberbullying detection (2013)

    Google Scholar 

  18. Nahar, V., Unankard, S., Li, X., Pang, C.: Sentiment analysis for effective detection of cyber bullying. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds.) APWeb 2012. LNCS, vol. 7235, pp. 767–774. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29253-8_75

    Chapter  Google Scholar 

  19. Nahar, V., Al-Maskari, S., Li, X., Pang, C.: Semi-supervised learning for cyberbullying detection in social networks. In: Wang, H., Sharaf, M.A. (eds.) ADC 2014. LNCS, vol. 8506, pp. 160–171. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08608-8_14

    Chapter  Google Scholar 

  20. Nalini, K., Sheela, L.J.: Classification of tweets using text classifier to detect cyber bullying. In: Emerging ICT for Bridging the Future-Proceedings of the 49th Annual Convention of the Computer Society of India CSI, vol. 2, pp. 637–645. Springer (2015)

    Google Scholar 

  21. NLTK: Python nltk library (2020). https://www.nltk.org/. Accessed 30 Mar 2020

  22. Patchin, J.W., Hinduja, S.: An update and synthesis of the research. Cyberbullying Prevention and Response: Expert Perspectives, p. 13 (2012)

    Google Scholar 

  23. Perez, S.: It’s time to pay serious attention to Tiktok (2019). https://techcrunch.com/2019/01/29/its-time-to-pay-serious-attention-to-tiktok/. Accessed 28 July 2020

  24. Ptaszynski, M., et al.: In the service of online order tackling cyberbullying with machine learning and affect analysis. Int. J. Comput. Linguist. Res. 1(3), 135–154 (2010)

    Google Scholar 

  25. Rafiq, R.I., Hosseinmardi, H., Han, R., Lv, Q., Mishra, S., Mattson, S.A.: Careful what you share in six seconds: detecting cyberbullying instances in vine. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, Paris, France, pp. 617–622. ACM (2015)

    Google Scholar 

  26. Sanchez, H., Kumar, S.: Twitter bullying detection. In: NSDI, pp. 15–15. USENIX Association, Berkeley, CA, USA (2012)

    Google Scholar 

  27. Silva, T.H., de Melo, P.O.S.V., Almeida, J.M., Salles, J., Loureiro, A.A.F.: A picture of Instagram is worth more than a thousand words: Workload characterization and application. In: 2013 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), pp. 123–132. IEEE (2013)

    Google Scholar 

  28. Smith-Spark, L.: Hanna smith suicide fuels calls for action on ask.fm cyberbullying, CNN (2013). http://www.cnn.com/2013/08/07/world/europe/uk-social-media-bullying/. Accessed 14 Jan 2014

  29. Soni, D., Singh, V.K.: See no evil, hear no evil: Audio-visual-textual cyberbullying detection. Proc. ACM Hum.-Comput. Interact. 2(CSCW), November 2018. https://doi.org/10.1145/3274433

  30. Xu, J.M., Jun, K.S., Zhu, X., Bellmore, A.: Learning from bullying traces in social media. In: NAACL HLT, pp. 656–666. Association for Computational Linguistics (2012)

    Google Scholar 

  31. Xu, J.M., Jun, K.S., Zhu, X., Bellmore, A.: Learning from bullying traces in social media. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 656–666. NAACL HLT 2012, Association for Computational Linguistics, USA (2012)

    Google Scholar 

  32. Yao, M., Chelmis, C., Zois, D.: Cyberbullying ends here: towards robust detection of cyberbullying in social media. In: The World Wide Web Conference, WWW 2019, pp. 3427–3433. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3308558.3313462

Download references

Acknowledgement

This work was supported by the US National Science Foundation (NSF) through grant CNS 1528138.

Author information

Authors and Affiliations

  1. University of Colorado Boulder, Boulder, USA

    Rahat Ibn Rafiq, Richard Han, Qin Lv & Shivakant Mishra

  2. University of Southern California, Los Angeles, CA, USA

    Homa Hosseinmardi

Authors
  1. Rahat Ibn Rafiq
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Homa Hosseinmardi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Richard Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qin Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shivakant Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rahat Ibn Rafiq .

Editor information

Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Juan Antonio Lossio-Ventura

  2. Visibilia, São Paulo, Brazil

    Jorge Carlos Valverde-Rebaza

  3. University of Valencia, Valencia, Spain

    Eduardo Díaz

  4. Universidad del Pacífico, Lima, Peru

    Hugo Alatrista-Salas

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rafiq, R.I., Hosseinmardi, H., Han, R., Lv, Q., Mishra, S. (2021). Identifying Differentiating Factors for Cyberbullying in Vine and Instagram. In: Lossio-Ventura, J.A., Valverde-Rebaza, J.C., Díaz, E., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2020. Communications in Computer and Information Science, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-76228-5_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-76228-5_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-76227-8

  • Online ISBN: 978-3-030-76228-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation