Abstract
Cyberbullying is a form of harassment that occurs through online communication with the intention of causing emotional distress to the intended target(s). Given the increase in cyberbullying, our goal is to develop a machine learning classification schema to minimize incidents specifically involving text extracted from image memes. To provide a current corpus for classification of the text that can be found in image memes, we collected a corpus containing approximately 19,000 text comments extracted from YouTube. We report on the efficacy of three machine learning classifiers, naive Bayes, Support Vector Machine, and a convolutional neural network applied to a YouTube dataset, and compare the results to an existing Formspring dataset. Additionally, we investigate algorithms for detecting cyberbullying in topic-based subgroups within the YouTube corpus.
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Acknowledgments
This work was supported by the following: Northeastern Illinois University’s Committee on Organized Research, Student Center for Science Engagement Summer Research Program, and Northeastern Illinois University Graduate Dean’s Research and Creative Activities Assistantship. We would also like to thank Dr. Francisco Iacobelli, Diyan Simeonov, Kenneth Santiago, Obsmara Ulloa, Jorge Garcia, and Mirna Salem for their participation in this research project.
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Trana, R.E., Gomez, C.E., Adler, R.F. (2021). Fighting Cyberbullying: An Analysis of Algorithms Used to Detect Harassing Text Found on YouTube. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1213. Springer, Cham. https://doi.org/10.1007/978-3-030-51328-3_2
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