# Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
## 2 Literature  
Phone addiction, which has become a \"disease\" in the modern world, has given rise to the concept of phubbing. The term \"phubbing\", or \"phone preoccupation\", has become a subject of increasing concern in recent years and is a phenomenon that can have serious effects on our social relationships and psychological well-being. Phubbing refers to when a person constantly checks or uses their phone while interacting with others. This action leads to a lack of attention to the other person and communication breakdowns. Recent studies reveal that phubbing is an important problem in relationships and social connections [12]. Research shows that the addictive act of looking at the phone screen has negative effects on individuals' social, physical, and psychological conditions. In addition to research revealing that phubbing reduces trust between people [18], there are studies indicating that the quality of communication decreases in such an environment [19; 20; 21; 22; 23]. Therefore, communication not only becomes mundane but the quality of the interaction is potentially reduced, threatening the structure of the relationship [24]. When the research studies were examined, it was found that these studies mostly focus on adults, namely university students and adolescents [25]. Although the number of studies in this field seems to be quantitatively high in the literature, the studies conducted in this field are traditional studies that specifically address the impact of phubbing on individuals [26; 27]. On the other hand, in addition to these traditional studies, there are also studies using machine learning methods. For example, the study by [PERSON] et al. [9] adopts an approach based on data mining and machine learning to identify patterns related to phishing behaviour. In their study, various models were developed based on the collected data. The study found that the developed addiction measures failed to accurately predict phishing. Moreover, as highlighted by the much higher explained variance, the machine learning approach is considered more suitable for predicting pubbing than traditional linear statistical methods. This study also emphasises that phubbing cannot be solely associated with addiction dynamics (such as age, social anxiety, and loneliness) and that modelling procedures that can account for non-linearity are needed to accurately assess users' phubbing levels. In [PERSON] et al. [28], a machine learning model is introduced, offering a departure from conventional analysis techniques. This research explores the potential conflict arising from self-phubbing and partner phubbing by constructing a model that connects these behaviours. The aim is to understand how self- and partner phubbing influence relationship satisfaction and, in turn, impact an individual's overall life satisfaction. [PERSON] and [PERSON] [12] investigated the factors that contribute to phubbing behaviour and delved into how individuals reshape social communication norms in response to changes in their own behaviour and the behaviour of others around them. Their study investigated how smartphone addiction can lead to phubbing behaviour and the potential effects of gender and various variables, and they developed a data collection and prediction model through Qualtrics, a software program. Thus, at the end of this artificial intelligence-supported study, it was found that phubbing is a direct consequence of problem atic smartphone use. However, it is noted that artificial intelligence was not completely used in the analysis in this study. In addition to this, the quantitative method of the questionnaire was applied in this study. Most of the data were obtained through a questionnaire. Therefore, it is not possible to state that this study was conducted entirely using artificial intelligence. In [PERSON] [11], the effect of a fear of missing out (FOMO) as a determinant of phubbing behaviour is investigated by machine learning methods.