{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T04:59:31Z","timestamp":1790312371894,"version":"4.1.0"},"reference-count":34,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Cyberpsychology, Behavior, and Social Networking"],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:p>\n                    The rise of generative artificial intelligence (GAI) has expanded the landscape of cyberbullying, necessitating an examination of the psychological and demographic factors underlying these behaviors. This two-wave longitudinal study (\n                    <jats:italic toggle=\"yes\">N<\/jats:italic>\n                    = 1,019) investigated the associations between Dark Tetrad, gender, age, and GAI-involved cyberbullying (GAICB) over a 6-month interval. Hierarchical linear regressions indicated that psychopathy and male gender were longitudinal predictors of GAICB at Time 2, even after controlling for baseline behaviors at Time 1. While sadism was significant in the baseline model, its effect became nonsignificant after accounting for prior GAICB. Machiavellianism, narcissism, and age showed no significant effects. These results suggest that psychopathy and gender may be associated with AI-driven aggression, warranting further research into targeted prevention strategies.\n                  <\/jats:p>","DOI":"10.1177\/21522715261465484","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T07:37:34Z","timestamp":1782805054000},"page":"650-656","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Longitudinal Predictors of Generative AI-Involved Cyberbullying: A Two-Wave Longitudinal Study"],"prefix":"10.1177","volume":"29","author":[{"given":"Rongjian","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Educational Psychology, Texas A&amp;M University, College Station, Texas, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0900-3905","authenticated-orcid":false,"given":"Cheng-Yen","family":"Wang","sequence":"additional","affiliation":[{"name":"Graduate Institute of Education, Tunghai University, Taichung, Taiwan."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"e_1_3_3_2_1","volume-title":"Principles of cyberbullying research: Definitions, measures, and methodology","author":"Bauman S","year":"2013","unstructured":"1.Bauman S, , Cross D, , Walker J. 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