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With the development of the Internet, the issue of cyberbullying on social media has gained significant attention. Cyberbullying is often expressed in text. Methods of identifying such text via machine learning have been growing, most of which rely on the extraction of part-of-speech (POS) tags to improve their performance. However, the current study only arbitrarily used part-of-speech labels that it considered reasonable, without investigating whether the chosen part-of-speech labels can better enhance the effectiveness of the cyberbullying detection task. In other words, the effectiveness of different part-of-speech labels in the automatic cyberbullying detection task was not proven. This study aimed to investigate the part of speech in statements related to cyberbullying and explore how three classification models (random forest, naïve Bayes, and support vector machine) are sensitive to parts of speech in detecting cyberbullying. We also examined which part-of-speech combinations are most appropriate for the models mentioned above. The results of our experiments showed that the predictive performance of different models differs when using different part-of-speech tags as inputs. Random forest showed the best predictive performance, and naive Bayes and support vector machine followed, respectively. Meanwhile, across the different models, the sensitivity to different part-of-speech tags was consistent, with greater sensitivity shown towards nouns, verbs, and measure words, and lower sensitivity shown towards adjectives and pronouns. We also found that the combination of different parts of speech as inputs had an influence on the predictive performance of the models. This study will help researchers to determine which combination of part-of-speech categories is appropriate to improve the accuracy of cyberbullying detection. [ABSTRACT FROM AUTHOR]

The Internet has become an essential part of our daily life, but excessive Internet use may lead to a number of risks such as Internet addiction. In order to protect teenagers from the risks, it is important to guide them to use the Internet in a safe, responsible, and ethical way. Cyber wellness literacy, as the core issue of digital citizenship, plays a vital role in the physical and mental well-being of individuals and should be given high priority. While some studies have explored the integration of digital citizenship into school education through digital game-based learning (DGBL), the influence of digital games on teenagers' learning outcomes, learning motivation, and engagement in the field of cyber wellness remains unclear. It is, therefore, a challenge to provide cyber wellness literacy learning activities that empower students to keep away from Internet addiction and maintain a happy, healthy, and safe digital life. This study addressed the issue of Internet addiction from the perspective of digital citizenship, and designed and implemented a digital game-based course in a middle school. The study then explored the potential impact of DGBL on improving students' cyber wellness literacy, motivation, and engagement. The statistical results show that DGBL not only promoted the students' cyber wellness literacy in preventing Internet addiction, but also enhanced their motivations and emotional engagement. [ABSTRACT FROM AUTHOR]