{"type":"ScholarlyArticle","title":"PI-Bully: Personalized Cyberbullying Detection with Peer Influence","authors":[{"name":"Lu Cheng","slug":"lu-cheng","given":"Lu","family":"Cheng","affiliation":"Computer Science and Engineering , Arizona State University"},{"name":"Jundong Li","slug":"jundong-li","given":"Jundong","family":"Li","affiliation":"Computer Science and Engineering , Arizona State University"},{"name":"Yasin Silva","slug":"yasin-silva","given":"Yasin","family":"Silva","affiliation":"Mathematical and Natural Sciences , Arizona State University"},{"name":"Deborah Hall","slug":"deborah-hall","given":"Deborah","family":"Hall","affiliation":"Social and Behavioral Sciences , Arizona State University"},{"name":"Huan Liu","slug":"huan-liu","given":"Huan","family":"Liu","affiliation":"Computer Science and Engineering , Arizona State University"}],"abstract":"Cyberbullying has become one of the most pressing online risks for adolescents and has raised serious concerns in society. Recent years have witnessed a surge in research aimed at developing principled learning models to detect cyberbullying behaviors. These efforts have primarily focused on building a single generic classification model to differentiate bullying content from normal (non-bullying) content among all users. These models treat users equally and overlook idiosyncratic information about users that might facilitate the accurate detection of cyberbullying. In this paper, we propose a personalized cyberbullying detection framework, PI-Bully, that draws on empirical findings from psychology highlighting unique characteristics of victims and bullies and peer influence from like-minded users as predictors of cyberbullying behaviors. Our framework is novel in its ability to model peer influence in a collaborative environment and tailor cyberbullying prediction for each individual user. Extensive experimental evaluations on real-world datasets corroborate the effectiveness of the proposed framework.","doi":"10.24963/ijcai.2019/808","url":"https://ijcai.org/proceedings/2019/808","pdf":"https://ijcai.org/proceedings/2019/0808.pdf","markdown":"https://ijcai.org/proceedings/2019/808.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/808","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"AI for Improving Human Well-being","track":"AI for Improving Human Well-being","number":808,"pages":{"first":5829,"last":5835},"keywords":["Special Track on AI for Improving Human-Well Being: AI safety (Special Track on AI and Human Wellbeing)","Special Track on AI for Improving Human-Well Being: Human wellbeing (Special Track on AI and Human Wellbeing)","Special Track on AI for Improving Human-Well Being: Societal applications (Special Track on AI and Human Wellbeing)","Special Track on AI for Improving Human-Well Being: AI applications for Improving Human-Well Being (Special Track on AI and Human Wellbeing)"],"videos":[],"references":[{"position":1,"raw":"[Al-garadi et al., 2016] Mohammed Ali Al-garadi, Kas- turi Dewi Varathan, and Sri Devi Ravana. 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