TY - GEN
T1 - Fighting Cyberbullying
T2 - AHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020
AU - Trana, Rachel E.
AU - Gomez, Christopher E.
AU - Adler, Rachel F.
N1 - Funding Information:
This work was supported by the following: Northeastern Illinois Univer-sity’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.
Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - CNN
KW - Cyberbullying
KW - Machine learning
KW - Naive Bayes
KW - SVM
KW - YouTube
UR - http://www.scopus.com/inward/record.url?scp=85088522248&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85088522248&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-51328-3_2
DO - 10.1007/978-3-030-51328-3_2
M3 - Conference contribution
AN - SCOPUS:85088522248
SN - 9783030513276
T3 - Advances in Intelligent Systems and Computing
SP - 9
EP - 15
BT - Advances in Artificial Intelligence, Software and Systems Engineering - Proceedings of the AHFE 2020 Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing
A2 - Ahram, Tareq
PB - Springer Netherlands
Y2 - 16 July 2020 through 20 July 2020
ER -