Cyberbullying is the wilful and repeated infliction of harm on an individual using the Internet and digital technologies. Similar to face-to-face bullying, cyberbullying can be captured formally using the Routine Activities Model (RAM) whereby the potential victim and bully are brought into proximity of one another via the interaction on online social networking (OSN) platforms. Although the impact of the COVID-19 (SARS-CoV-2) restrictions on the online presence of minors has yet to be fully grasped, studies have reported that 44% of pre-adolescents have encountered more cyberbullying incidents during the COVID-19 lockdown. Transparency reports shared by OSN companies indicate an increased take-downs of cyberbullying-related comments, posts or content by artificially intelligen moderation tools. However, in order to efficiently and effectively detect or identify whether a social media post or comment qualifies as cyberbullying, there are a number factors based on the RAM, which must be taken into account, which includes the identification of cyberbullying roles and forms. This demands the acquisition of large amounts of fine-grained annotated data which is costly and ethically challenging to produce. In addition where fine-grained datasets do exist they may be unavailable in the target language. Manual translation is costly and expensive, however, state-of-the-art neural machine translation offers a workaround. This study presents a first of its kind experiment in leveraging machine translation to automatically translate a unique pre-adolescent cyberbullying gold standard dataset in Italian with fine-grained annotations into English for training and testing a native binary classifier for pre-adolescent cyberbullying. In addition to contributing high-quality English reference translation of the source gold standard, our experiments indicate that the performance of our target binary classifier when trained on machine-translated English output is on par with the source (Italian) classifier.
In this paper the architecture and subsystems of the h-Life platform are described. The system is currently under implementation and therefore all system components and modules are described according to their design specifications. h-Life aims at developing a user-friendly, personalized and ubiquitously accessible knowledge-based system that will provide citizens with an intelligent tool for monitoring the state of health and lifestyle. The h-Life system is based on the integration of several components: • a knowledge repository related to health, nutrition, fitness and lifestyle• a user repository where personal health records and user profiles are stored• a plan repository for the storage of medical plans• an intelligent adviser and plan selection mechanism, which provides advice and plans based on the user profile• an alerting and reminding module that issues alerts (to ensure the timely execution of a selected plan) and notifications concerning events of potential interest• a customer-facing module, which ensures personalized interaction with the system and customizes the user-system interaction according to the user profile.Based on the latest technology in intelligent and communication systems, the h-Life environment aims at providing the user with personalized, ‘certified’ lifestyle information and consultancy, tailored to individual needs and available anywhere and at anytime.
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