{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T05:35:58Z","timestamp":1768455358558,"version":"3.49.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/808","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"5829-5835","source":"Crossref","is-referenced-by-count":23,"title":["PI-Bully: Personalized Cyberbullying Detection with Peer Influence"],"prefix":"10.24963","author":[{"given":"Lu","family":"Cheng","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jundong","family":"Li","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasin","family":"Silva","sequence":"additional","affiliation":[{"name":"Mathematical and Natural Sciences, Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deborah","family":"Hall","sequence":"additional","affiliation":[{"name":"Social and Behavioral Sciences, Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Liu","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:51:57Z","timestamp":1564285917000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/808"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/808","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}