###### Abstract  
Since there are growing concerns regarding online privacy, firms may have the risk of being involved in various privacy infringement cases resulting in legal causations. If firms are aware of consequences from possible cases of invasion of online privacy, they can more actively prevent future online privacy infringements. Thus, this study attempts to predict the probability of judgment types caused by various invasions within US judicial cases that are related to online privacy invasions. Since legal judgment results are significantly influenced by societal factors and technological development, this study tries to identify a model that can accurately predict legal judgment with explainability. To archive the study objective, it compares the prediction performance by applying five types of classification algorithms (LDA, NNET, CART, SVM, and random forest) of machine learning. We also examined the relationship between privacy infringement factors and adjudications by applying network text analysis. The results indicate that firms could have a high possibility of both civil and criminal law responsibilities if they distributed malware or spyware, intentionally or non-intentionally, to collect unauthorized data. It addresses the needs of reflecting both quantitative and qualitative approach for establishing automatic legal systems for improving its accuracy based on the socio-technical perspective. online privacy invasions; personal information infringements; predicting judgments; predictive analytics; privacy act; network text analysis +
Footnote †: journal: Applied Sciences  
15 October 2021 17 November 2021  
factors with high potential to cause privacy infringements and thus constitute illegal acts, as well as to compare the performance of predicting judgments. This study focuses on the judicial cases related to privacy infringements caused by firms [2]. Since online privacy infringement cases cause severe legal consequences for firms, it is important for them to be aware of various privacy infringement with legal liability. In addition, as the number of online privacy invasion cases is predicted to increase and become more diverse, the development of an automated predictive model for legal decisions can significantly reduce the efforts of legal practitioners as well as companies. Therefore, this study is devised from the motivation to suggest the foundation for applying and interpreting a predictive model from a socio-technical perspective, along with deriving an optimal judgment model. Legal cases related to privacy infringement are outcomes of interactions between society and technological factors, since the cases often comprise violation of law, human errors, personal information owners' perception, etc. It means that recent privacy legal cases have shown different characteristics from other legal incidents. For example, the case of stealing someone's property is always considered illegal, regardless of time and space. However, in most privacy infringement cases, it is very difficult to clearly identify a responsible party. Legal cases regarding improper usage of adhere to invade an individual's privacy is a representative case that demonstrates that legal judgments may be influenced by technological and social environmental factors. Therefore, this study applied NTA (network text analysis) method, which has been used in sociology to reflect social influences [3]. In addition, we performed a comparison of AI models, including LDA (linear discriminant analysis), NNET (neural network), CART (classification and regression tree), SVM (support vector machine), and random forest, to identify a model with high prediction accuracy. The results of this study provide the foundation for developing an automated legal prediction system that could consider influences from social and technical factors that past research did not consider. This study is conducted in the following steps.