Detecting the Presence of Cyberbullying Using Computer Software

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Bayzick, Cyberbullying 14or parts of the conversation. In terms of the language used, outing is generallysimilar to denigration, although usually less vulgar and volatile [4].ix. ExclusionIn the survey conducted by Patchin and Hinduja, exclusion, or ignoring the victimin a chat room or conversation, was the type of cyberbullying reported to havehappened most often among youth and teens [3]. However, detecting exclusionwithout contact from the victim outside the chat room is not straight forward. Ifthe victim reacts in a way that indicates they think they are being ignored (i.e.“hey guys, are you listening???”), then it can be concluded that exclusion hashappened. However, if the victim brings up a topic or asks a question, but thesubject is never responded to or brought up again, this constitutes exclusion butmay not be as easily distinguished [4].
Bayzick, Cyberbullying 15IV DatasetOur dataset consists of chat transcripts crawled from MySpace.com. Theconversations are thread-style forums, where a general topic is included in the creation ofthe thread. Many users can post in the thread, and conversation usually deviates from thestarting topic. When working with these conversations, we considered a post to be a singlebody of chat text posted by a user at one time. The body of text could contain multiplesentences or even multiple paragraphs and still be considered a single post. Because of theinteractive nature of cyberbullying, the conversations were processed using a movingwindow of 10 posts to capture context.Undergraduate research assistants from the Media and Communications StudiesDepartment developed a truth set for testing our algorithms. These individuals were givendirections for coding found in the codebook in Appendix A along with the definitions foundin sections III and IV. They reviewed each window and indicated whether or notcyberbullying is present. Three assistants coded each transcript, and a voting method wasused, where a window was considered to contain cyberbullying in the truth set if at leasttwo humans flagged it as such. These labelers also identified the type of cyberbullying andthe lines that are involved in the instance of cyberbullying, but this information is notcurrently being used. Interestingly, though the coders generally agreed on the presence orlack of cyberbullying in a window, they varied widely in their classification of an instance ofcyberbullying. This can suggest either that our category definitions need clarification or thatthe language is inherently ambiguous. In later stages of the project, the algorithms will befine-tuned in order to detect the distinction between different categories. It should be
Page 1 and 2: Detecting the Presence of Cyberbull
Page 3 and 4: Bayzick, Cyberbullying 3AbstractCyb
Page 5 and 6: Bayzick, Cyberbullying 5I Introduct
Page 7 and 8: Bayzick, Cyberbullying 7o“Dude to
Page 9 and 10: Bayzick, Cyberbullying 9III Types o
Page 11 and 12: Bayzick, Cyberbullying 11masquerade
Page 13: Bayzick, Cyberbullying 13vii. Denig
Page 17 and 18: Bayzick, Cyberbullying 17V ChatCode
Page 19 and 20: Bayzick, Cyberbullying 19insult to
Page 21 and 22: Bayzick, Cyberbullying 21VII BullyT
Page 23 and 24: Bayzick, Cyberbullying 23VIII Evalu
Page 25 and 26: Bayzick, Cyberbullying 25However, t
Page 27 and 28: Bayzick, Cyberbullying 27X Acknowle
Page 29 and 30: Bayzick, Cyberbullying 29Appendix A
Page 31 and 32: Bayzick, Cyberbullying 315. Lots of
Page 33: Bayzick, Cyberbullying 33douchebagi

Detecting the Presence of Cyberbullying Using Computer Software

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