Detecting the Presence of Cyberbullying Using Computer Software

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Bayzick, Cyberbullying 26IX Conclusion and Further ResearchThis project defines nine types of cyberbullying and proposes methods to detect thepresence of these types in online chat conversations. It also describes the firstimplementation of algorithms to detect the presence of cyberbullying on a window level inthe program called BullyTracer. Further research includes refining these methods andexpanding them to distinguish between different types of cyberbullying. Throughout thisproject, the only data available for examination was a set of thread-style conversations fromwww.myspace.com.BullyTracer was found to correctly identify windows containing cyberbullying 85.30%of the time, and it identifies an innocent window correctly 51.91% of the time. Overall, itdecides correctly 58.63% of the windows. This suggests that our coding rules must berefined to not falsely flag so much innocent conversation.Future work on the project includes examining instances where BullyTracer codesincorrectly more closely, particularly the false positives, and refining BullyTracer’s rulesaccordingly. The importance of capital letters and swear words should also be examinedmore deeply. More ambitious goals include examining the role of context in distinguishingbetween types of cyberbullying and developing rules to classify windows into one of thenine cyber-bullying categories we define in this work.One of the most important contributions of this work was the creation of theMySpace cyberbullying truth set. There was previously no truth set in existence forcyberbullying, and so ours has been requested by a number of other research teams.
Bayzick, Cyberbullying 27X AcknowledgementsThe author would like to thank Dr. April Kontostathis and Dr. Lynne Edwards for all oftheir help and support, as well as India McGhee, Bryan Stafford, Brett Thom, Rosie Kress, LizKilmer, Magdalena Parks, Brett Neslen, Sarah Anne Rosner, Katie Haldeman and the UrsinusCollege Summer Fellows program. This material is based upon work supported by theNational Science Foundation under Grant No. 0916152. Any opinions, findings, andconclusions or recommendations expressed in this material are those of the author(s) anddo not necessarily reflect the views of the National Science Foundation.
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 and 14: Bayzick, Cyberbullying 13vii. Denig
Page 15 and 16: Bayzick, Cyberbullying 15IV Dataset
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
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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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