International Journal of Information Technology and Computer Science(IJITCS)
ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)
Published By: MECS Press
IJITCS Vol.11, No.5, May. 2019
Development of Aggression Detection Technique in Social Media
Full Text (PDF, 543KB), PP.40-46
Due to the enormous growth of social media the potential of social media mining has increased exponentially. Individual users are producing data at unprecedented rate by sharing and interacting through social media. This user generated data provides opportunities to explore what people think and express on social media. Users exhibit different behaviors on social media towards individuals, a group, a topic or an activity. In this paper, we present a social media mining approach to perform behavior analytics. In this research study, we performed a descriptive analysis of user generated data such as users’ status, comments and replies to identify individual users or groups which can be a potential threat. Tokenization technique is used to estimate the polarity of the behavior of different users by considering their comments or feedbacks against different posts on Facebook. The proposed approach can help to identify possible threats reflected by the user’s behavior towards a specific event. To evaluate the approach, a data set was developed containing comments on the Facebook from different users in different groups. The dataset was divided into different groups such as political, religious and sports. Most negative users’ in different groups were identified successfully. In our research, we focused only on English content; however, it can be evaluated with other languages.
Cite This Paper
Shah Zaib, Muhammad Asif, Maha Arooj, "Development of Aggression Detection Technique in Social Media", International Journal of Information Technology and Computer Science(IJITCS), Vol.11, No.5, pp.40-46, 2019. DOI: 10.5815/ijitcs.2019.05.05
Kansara, K.B. and N.M. Shekokar, A framework for cyberbullying detection in social network. International Journal of Current Engineering and Technology, 2015. 5(1): p. 494-498. E-ISSN 2277 – 4106, P-ISSN 2347 – 5161
Neri, F., et al. Sentiment analysis on social media. in 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2012. IEEE. DOI: 10.1109/ASONAM.2012.164
Tan, P.-N., M. Steinbach, and V. Kumar, Introduction to data mining: Pearson addison wesley. Boston, 2005.
Han, J., J. Pei, and M. Kamber, Data mining: concepts and techniques. 2011: Elsevier.
Kantardzic, M., Data mining: concepts, models, methods, and algorithms. 2011: John Wiley & Sons.
Gundecha, P. and H. Liu, Mining social media: a brief introduction, in New Directions in Informatics, Optimization, Logistics, and Production. 2012, Informs. p. 1-17. http://dx.doi.org/10.1287/educ.1120.0105.
Kumar, D. and D. Bhardwaj, Rise of data mining: current and future application areas. International Journal of Computer Science Issues (IJCSI), 2011. 8(5): p. 256.
Berk, R., et al., Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning. Journal of the Royal Statistical Society: Series A (Statistics in Society), 2009. 172(1): p. 191-211. 0964–1998/09/172000.
Gwinn, S.L., et al., Exploring crime analysis: Readings on essential skills. 2008: International Association of Crime Analysts.
Wang, T., et al. Learning to detect patterns of crime. in Joint European conference on machine learning and knowledge discovery in databases. 2013. Springer 515–530, 2013.
News Room: Available from: URL: http://newsroom.fb.com/company-info/,. 06/02/2016.
Bretschneider, U. and R. Peters. Detecting offensive statements towards foreigners in social media. in Proceedings of the 50th Hawaii International Conference on System Sciences. 2017, ISBN: 978-0-9981331-0-2.
King, R.A., P. Racherla, and V.D. Bush, What we know and don't know about online word-of-mouth: A review and synthesis of the literature. Journal of interactive marketing, 2014. 28(3): p. 167-183, http://dx.doi.org/10.1016/j.intmar.2014.02.001 .
Brody, S. and N. Diakopoulos. Cooooooooooooooollllllllllllll!!!!!!!!!!!!!!: using word lengthening to detect sentiment in microblogs. in Proceedings of the conference on empirical methods in natural language processing. 2011. Association for Computational Linguistics, ISBN: 978-1-937284-11-4.
Nobata, C., et al. Abusive language detection in online user content. in Proceedings of the 25th international conference on world wide web. 2016. International World Wide Web Conferences Steering Committee.
Silge, J. and D. Robinson, Text mining with R: A tidy approach. 2017: "O'Reilly Media, Inc.".
Kandias, M., et al. Proactive insider threat detection through social media: The YouTube case. in Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society. 2013. ACM.
Bolla, R.A., Crime pattern detection using online social media. 2014.
Weinstein, C., et al. Modeling and detection techniques for counter-terror social network analysis and intent recognition. in 2009 IEEE Aerospace conference. 2009. IEEE, http://dx.doi.org/10.1109/AERO.2009.4839642.
Nath, S.V. Crime pattern detection using data mining. in 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops. 2006. IEEE.
Silge, J. and D. Robinson, tidytext: Text mining and analysis using tidy data principles in r. The Journal of Open Source Software, 2016. 1(3): p. 37.