Abstract
Nowadays, cyberbullying issues have rapidly arisen with various type of negatives impacts have been affected millions of users. Students’ academic performance might be affected due to being a cyberbullying victim. A social networking service, Twitter is one of the most used platforms worldwide has significantly increase on the activity of cyberbullying even though this platform limits the words characters up to 280 only. Due to this activity which involving online bullying, this paper aims to develop an awareness with some potential detection platform where users can take an advance action to block or report any Twitter account that involving with cyberbullying activity. Consequently, it could help them to have a positive as well as better online environment.
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References
A.M.B. AditiaRakhmat Sentiaji et al. Analisis Sentimen Terhadap Acara Televisi Berdasarkan Opini Publik. Jurnal Ilmiah Komputer dan Informatika (KOMPUTA) (2014)
A. Agarwal, F. Biadsy, K.R. McKeown, Contextual phrase-level polarity analysis using lexical affect scoring and syntactic N-grams, in EACL 2009 - 12th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings (April, 2009), pp. 24–32. https://doi.org/10.3115/1609067.1609069
M. Dadvar et al., Improving cyberbullying detection with user context, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7814 LNCS (2013), pp. 693–696. https://doi.org/10.1007/978-3-642-36973-5_62
End To Cyber Bullying Organization. (n.d.). Twitter is Turning Into a Cyber Bullying Playground. Retrieved from End To Cyber Bullying Organization: https://www.endcyberbullying.org/twitter-is-turning-into-a-cyber-bullying-playground/
S. Hinduja, J.W. Patchin, Connecting adolescent suicide to the severity of bullying and cyberbullying. J. Sch. Violence 18(3), 333–346 (2019). https://doi.org/10.1080/15388220.2018.1492417
H. Hosseinmardi et al., Analyzing labeled cyberbullying incidents on the instagram social network, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9471 (2015), pp. 49–66. https://doi.org/10.1007/978-3-319-27433-1_4
C. Langos, Cyberbullying: The challenge to define. Cyberpsychol. Behav. Soc. Netw. 15(6), 285–289 (2012). https://doi.org/10.1089/cyber.2011.0588
W. Medhat, A. Hassan, H. Korashy, Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014). https://doi.org/10.1016/j.asej.2014.04.011
MonkeyLearn, Sentiment Analysis: A Definitive Guide. Retrieved from MonkeyLearn: https://monkeylearn.com/sentiment-analysis/ (2020)
A. Muneer, S.M. Fati, A comparative analysis of machine learning techniques for cyberbullying detection on twitter. Futur. Internet 12(11), 1–21 (2020). https://doi.org/10.3390/fi12110187
M.Z. Naf’an, et al., Sentiment analysis of cyberbullying on instagram user comments. J. Data Sci. Its Appl. 2(1), 88–98 (2019). https://doi.org/10.21108/jdsa.2019.2.20
D. Olweus, Invited expert discussion paper cyberbullying: an overrated phenomenon? Eur. J. Dev. Psychol. 9(5), 520–538 (2012)
S. Sims, KDnuggets. Retrieved from Sentiment Analysis, 101 (December, 2015). https://www.kdnuggets.com/2015/12/sentiment-analysis-101.html
M. Sintaha, N. Zawad, Cyberbullying detection using sentiment analysis in social (2016)
A. Srivastava, V. Singh, G.S. Drall, Sentiment analysis of twitter data: a hybrid approach. Int. J. Healthc. Inf. Syst. Inform. 14(2), 1–16 (2019). https://doi.org/10.4018/IJHISI.2019040101
Statista, Number of monthly active Twitter users worldwide from 1st quarter 2010 to 1st quarter 2019 (2020). Retrieved from statista: https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/
L. P. v Bosque, S.E. Garza, Aggressive text detection for cyberbullying, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8856 (2014), pp. 221–232. https://doi.org/10.1007/978-3-319-13647-9_21
A. Zuckerman, 60 Cyberbullying Statistics: 2019/2020 Data, Insights & Predictions (12 May, 2020). Retrieved from CompareCamp: https://comparecamp.com/cyberbullying-statistics/
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Ahmad, I.S., Darmawan, M.F., Talib, C.A. (2023). Cyberbullying Awareness Through Sentiment Analysis Based on Twitter. In: Yafooz, W.M.S., Al-Aqrabi, H., Al-Dhaqm, A., Emara, A. (eds) Kids Cybersecurity Using Computational Intelligence Techniques. Studies in Computational Intelligence, vol 1080. Springer, Cham. https://doi.org/10.1007/978-3-031-21199-7_14
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