An Enhanced Model for Abusive Behavior Detection in Social Network

  • Kefaya QaddoumEmail author
  • Israr Ahmad
  • Yasir Javed
  • Ali Rodan
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 29)


Due to the growing use of social media, incidents of online abuse are also on rise. Online abusive behavior is defined as the use of electronic devices connected through internet for offensive activities. It is mostly in the form of comments containing abusive words about others, which affect the target users’ psychology and depresses them. This paper is aimed at devising method for detecting abusive behavior using supervised learning techniques. Two hypotheses are presented to extract features for detection of offensive comments. The initial experiments show that using features using our proposed method has better accuracy than the traditional feature extraction techniques like TF-IDF.


  1. 1.
    Pickering, T.A., Yuen, T.T., Wang, T.: STEM conversations in social media: implications on STEM education. In: 2016 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), Bangkok, pp. 296–302 (2016).
  2. 2.
    Li, Q., Liu, P.: Detecting user behavior anomalies in communication networks. In: 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, pp. 384–388 (2017).
  3. 3.
    Chen, Y., Lv, Y., Wang, X., Wang, F.Y.: A convolutional neural network for traffic information sensing from social media text. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, pp. 1–6 (2017).
  4. 4.
    Lourentzou, I., Morales, A., Zhai, C.: Text-based geolocation prediction of social media users with neural networks. In: 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, pp. 696–705 (2017).
  5. 5.
    Ivaschenko, A., Khorina, A., Isayko, V., Krupin, D., Bolotsky, V., Sitnikov, P.: Modeling of user behavior for social media analysis. In: 2018 Moscow Workshop on Electronic and Networking Technologies (MWENT), Moscow, pp. 1–4 (2018).
  6. 6.
    Gupta, S., Singhal, A.: Phishing URL detection by using artificial neural network with PSO. In: 2nd International Conference on Telecommunication and Networks (TEL-NET), Noida, pp. 1–6 (2017).
  7. 7.
    Li, H.: Centrality analysis of online social network big data. In: 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA), Shanghai, pp. 38–42 (2018).
  8. 8.
    Whittaker, E., Kowalski, R.M.: Abusive behavior via social media. J. Sch. Violence 14(1), 11–29 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kefaya Qaddoum
    • 1
    Email author
  • Israr Ahmad
    • 1
  • Yasir Javed
    • 1
  • Ali Rodan
    • 1
  1. 1.Higher College of TechnologyAl Ain Women’s CollegeAbu DhabiUAE

Personalised recommendations