A socio-contextual approach in automated detection of cyberbullying

Nargess Tahmasbi, Elham Rastegari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Cyberbullying is a major cyber issue that is common among adolescents. Recent reports show that more than one out of five students in the United States is a victim of cyberbullying. Majority of cyberbullying incidents occur on public social media platforms such as Twitter. Automated cyberbullying detection methods can help prevent cyberbullying before the harm is done on the victim. In this study, we analyze a corpus of cyberbullying Tweets to construct an automated detection model. Our method emphasizes on the two claims that are supported by our results. First, despite other approaches that assume that cyberbullying instances use vulgar or profane words, we show that they do not necessarily contain negative words. Second, we highlight the importance of context and the characteristics of actors involved and their position in the network structure in detecting cyberbullying rather than only considering the textual content in our analysis.

Original languageEnglish (US)
Title of host publicationProceedings of the 51st Annual Hawaii International Conference on System Sciences, HICSS 2018
EditorsTung X. Bui
PublisherIEEE Computer Society
Pages2151-2160
Number of pages10
ISBN (Electronic)9780998133119
StatePublished - 2018
Event51st Annual Hawaii International Conference on System Sciences, HICSS 2018 - Big Island, United States
Duration: Jan 2 2018Jan 6 2018

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume2018-January
ISSN (Print)1530-1605

Conference

Conference51st Annual Hawaii International Conference on System Sciences, HICSS 2018
Country/TerritoryUnited States
CityBig Island
Period1/2/181/6/18

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint

Dive into the research topics of 'A socio-contextual approach in automated detection of cyberbullying'. Together they form a unique fingerprint.

Cite this