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The growing prevalence and rapid evolution of offensive language in socialmedia amplify the complexities of detection, particularly highlighting thechallenges in identifying such content across diverse languages. This surveypresents a systematic and comprehensive exploration of Cross-Lingual TransferLearning (CLTL) techniques in offensive language detection in social media. Ourstudy stands as the first holistic overview to focus exclusively on thecross-lingual scenario in this domain. We analyse 67 relevant papers andcategorise these studies across various dimensions, including thecharacteristics of multilingual datasets used, the cross-lingual resourcesemployed, and the specific CLTL strategies implemented. According to "what totransfer", we also summarise three main CLTL transfer approaches: instance,feature, and parameter transfer. Additionally, we shed light on the currentchallenges and future research opportunities in this field. Furthermore, wehave made our survey resources available online, including two comprehensivetables that provide accessible references to the multilingual datasets and CLTLmethods used in the reviewed literature.

Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges